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A Scalable Formal Approach for Correctness-Assured Hardware Design Speaker: Dr. Jin Yang, Intel Labs Date: March 8, 2023 Correctness must be a first principle in hardware design, especially for security and safety critical applications. We will give an overview of our scalable approach for correctness-assured hardware design at behavioral level, based on formalizing microarchitecture features as program transformations in an incremental compiler design and microprocessor correctness as a refined notation of compiler correctness. We will show how our approach is applied to designing a formally verified FHE (Fully Homo-morphic Encryption) accelerator. Speaker bio: Jin Yang is a Senior Principal Engineer in Strategic CAD Lab, Intel Labs. He is responsible for applied formal methods research for system software, hardware and security specification, design and verification. He joined Intel in 1997 after receiving his Ph.D. in CS from University of Texas at Austin, and has led many high-impact research projects and initiatives in Intel. He is active in the CAD and formal methods research communities with over 50 publications.	common_crawl_stanford.edu_350
Stanford AI Lab The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963. Latest News Carlos Guestrin named as new Director of the Stanford AI Lab! We thank Christopher Manning for being Director of the Stanford AI Lab during a period of enormous growth for AI and SAIL from 2018–2025 and today welcome Carlos Guestrin, Fortinet Founders Professor of Computer Science, as the new Director of SAIL. Congratulations to Prof. Fei-Fei Li for being one of the seven engineers who have made seminal contributions to the development of Modern Machine Learning awarded the 2025 Queen Elizabeth Prize for Engineering. Congratulations, Emma for being elected as AAAI Fellow! Congratulations to Chelsea Finn, Dorsa Sadigh, and Sanmi Koyejo for all winning a Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor of the U.S. government for outstanding early career scientists and engineers. Congratulations to Sebastian Thrun for receiving honorary doctorate from Geogia Tech! Sebastian Thrun received an honorary Doctorate from Georgia Tech! This is Sebastian’s fourth honorary doctorate. Sebastian also served as a speaker for Georgia Tech’s Fall commencement ceremony Ellen Vitercik received a Schmidt Science 2024 AI2050 Early Career Fellow Award Tatsu Hashimoto and Shuran Song both received “Samsung AI Researcher of the Year” awards Congratulations to Stanford AI Lab PhD student Dora Zhao for an ICML 2024 Best Paper Award! Congratulations to Stanford AI Lab PhD student Dora Zhao for an ICML 2024 Best Paper Award for a paper from her work at Sony AI on: Measure Dataset Diversity, Don’t Just Claim It Congratulations to Aaron Lou, Chenlin Meng, and Stefano Ermon for an ICML 2024 Best Paper Award! Congratulations to Aaron Lou, Chenlin Meng, and Stefano Ermon for an ICML 2024 Best Paper Award: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution Congratulations to Marco Pavone for a Robotics: Science and Systems Conference Best Paper Award! Congratulations to Marco Pavone for winning the best paper award at the Robotics: Science and Systems Conference on AI Safety for autonomous systems. Congratulations to Carlos Guestrin for being elected to the NAE! Carlos Guestrin has been elected to the National Academic of Engineering “for scalable systems and algorithms enabling the broad application of machine learning in science and industry.” Congratulations to Chris Manning on being awarded 2024 IEEE John von Neumann Medal! Chris Manning has been awarded the 2024 IEEE John von Neumann Medal “for advances in computational representation and analysis of natural language.” This is one of IEEE’s top awards in computing, given with very broad scope “for outstanding achievements in computer-related science and technology.” SAIL Faculty and Students Win NeurIPS Outstanding Paper Awards Congratulations to Sanmi Koyejo and his students for winning the NeurIPS Outstanding Paper Award, and congradulations to Chris Manning, Stefano Ermon, Chelsea Finn, and their students for winning Outstanding Paper Runner Up at NeurIPS! Prof. Fei Fei Li featured in CBS Mornings the Age of AI Follow Prof. Li's interview with CBS Mornings and on being named the "Godmother of AI" We Are Pleased to Welcome New Members of Our Faculty Diyi Yang who focuses on Computational Social Science and Natural Language Processing Sanmi Koyejo who focuses on Trustworthy Machine Learning for Healthcare and Neuroscience Affiliates Program Stanford AI Lab faculty and students enjoy chances to understand and solve the not-yet-doable pain points of industry. Get a chance to support and interact with SAIL’s brightest minds. Learn More	common_crawl_stanford.edu_351
Appendix I: A Short History of AI This Appendix is based primarily on Nilsson's book[140] and written from the prevalent current perspective, which focuses on data intensive methods and big data. However important, this focus has not yet shown itself to be the solution to all problems. A complete and fully balanced history of the field is beyond the scope of this document. The field of Artificial Intelligence (AI) was officially born and christened at a workshop organized by John McCarthy in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence. The goal was to investigate ways in which machines could be made to simulate aspects of intelligence—the essential idea that has continued to drive the field forward ever since. McCarthy is credited with the first use of the term “artificial intelligence” in the proposal he co-authored for the workshop with Marvin Minsky, Nathaniel Rochester, and Claude Shannon.[141] Many of the people who attended soon led significant projects under the banner of AI, including Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, and Herbert Simon. Although the Dartmouth workshop created a unified identity for the field and a dedicated research community, many of the technical ideas that have come to characterize AI existed much earlier. In the eighteenth century, Thomas Bayes provided a framework for reasoning about the probability of events.[142] In the nineteenth century, George Boole showed that logical reasoning—dating back to Aristotle—could be performed systematically in the same manner as solving a system of equations.[143] By the turn of the twentieth century, progress in the experimental sciences had led to the emergence of the field of statistics,[144] which enables inferences to be drawn rigorously from data. The idea of physically engineering a machine to execute sequences of instructions, which had captured the imagination of pioneers such as Charles Babbage, had matured by the 1950s, and resulted in the construction of the first electronic computers.[145] Primitive robots, which could sense and act autonomously, had also been built by that time.[146] The most influential ideas underpinning computer science came from Alan Turing, who proposed a formal model of computing. Turing's classic essay, Computing Machinery and Intelligence,[147] imagines the possibility of computers created for simulating intelligence and explores many of the ingredients now associated with AI, including how intelligence might be tested, and how machines might automatically learn. Though these ideas inspired AI, Turing did not have access to the computing resources needed to translate his ideas into action. Several focal areas in the quest for AI emerged between the 1950s and the 1970s.[148] Newell and Simon pioneered the foray into heuristic search, an efficient procedure for finding solutions in large, combinatorial spaces. In particular, they applied this idea to construct proofs of mathematical theorems, first through their Logic Theorist program, and then through the General Problem Solver.[149] In the area of computer vision, early work in character recognition by Selfridge and colleagues[150] laid the basis for more complex applications such as face recognition.[151] By the late sixties, work had also begun on natural language processing.[152] “Shakey”, a wheeled robot built at SRI International, launched the field of mobile robotics. Samuel's Checkers-playing program, which improved itself through self-play, was one of the first working instances of a machine learning system.[153] Rosenblatt's Perceptron,[154] a computational model based on biological neurons, became the basis for the field of artificial neural networks. Feigenbaum and others advocated [155]the case for building expert systems—knowledge repositories tailored for specialized domains such as chemistry and medical diagnosis.[156] Early conceptual progress assumed the existence of a symbolic system that could be reasoned about and built upon. But by the 1980s, despite this promising headway made into different aspects of artificial intelligence, the field still could boast no significant practical successes. This gap between theory and practice arose in part from an insufficient emphasis within the AI community on grounding systems physically, with direct access to environmental signals and data. There was also an overemphasis on Boolean (True/False) logic, overlooking the need to quantify uncertainty. The field was forced to take cognizance of these shortcomings in the mid-1980s, since interest in AI began to drop, and funding dried up. Nilsson calls this period the “AI winter.” A much needed resurgence in the nineties built upon the idea that “Good Old-Fashioned AI”[157] was inadequate as an end-to-end approach to building intelligent systems. Rather, intelligent systems needed to be built from the ground up, at all times solving the task at hand, albeit with different degrees of proficiency.[158] Technological progress had also made the task of building systems driven by real-world data more feasible. Cheaper and more reliable hardware for sensing and actuation made robots easier to build. Further, the Internet’s capacity for gathering large amounts of data, and the availability of computing power and storage to process that data, enabled statistical techniques that, by design, derive solutions from data. These developments have allowed AI to emerge in the past two decades as a profound influence on our daily lives, as detailed in Section II. In summary, following is a list of some of the traditional sub-areas of AI. As described in Section II, some of them are currently “hotter” than others for various reasons. But that is neither to minimize the historical importance of the others, nor to say that they may not re-emerge as hot areas in the future. - Search and Planning deal with reasoning about goal-directed behavior. Search plays a key role, for example, in chess-playing programs such as Deep Blue, in deciding which move (behavior) will ultimately lead to a win (goal). - The area of Knowledge Representation and Reasoning involves processing information (typically when in large amounts) into a structured form that can be queried more reliably and efficiently. IBM's Watson program, which beat human contenders to win the Jeopardy challenge in 2011, was largely based on an efficient scheme for organizing, indexing, and retrieving large amounts of information gathered from various sources.[159] - Machine Learning is a paradigm that enables systems to automatically improve their performance at a task by observing relevant data. Indeed, machine learning has been the key contributor to the AI surge in the past few decades, ranging from search and product recommendation engines, to systems for speech recognition, fraud detection, image understanding, and countless other tasks that once relied on human skill and judgment. The automation of these tasks has enabled the scaling up of services such as e-commerce. - As more and more intelligent systems get built, a natural question to consider is how such systems will interact with each other. The field of Multi-Agent Systems considers this question, which is becoming increasingly important in on-line marketplaces and transportation systems. - From its early days, AI has taken up the design and construction of systems that are embodied in the real world. The area of Robotics investigates fundamental aspects of sensing and acting—and especially their integration—that enable a robot to behave effectively. Since robots and other computer systems share the living world with human beings, the specialized subject of Human Robot Interaction has also become prominent in recent decades. - Machine perception has always played a central role in AI, partly in developing robotics, but also as a completely independent area of study. The most commonly studied perception modalities are Computer Vision and Natural Language Processing, each of which is attended to by large and vibrant communities. - Several other focus areas within AI today are consequences of the growth of the Internet. Social Network Analysis investigates the effect of neighborhood relations in influencing the behavior of individuals and communities. Crowdsourcing is yet another innovative problem-solving technique, which relies on harnessing human intelligence (typically from thousands of humans) to solve hard computational problems. Although the separation of AI into sub-fields has enabled deep technical progress along several different fronts, synthesizing intelligence at any reasonable scale invariably requires many different ideas to be integrated. For example, the AlphaGo program[160] [161] that recently defeated the current human champion at the game of Go used multiple machine learning algorithms for training itself, and also used a sophisticated search procedure while playing the game. [140] Nilsson, The Quest for Artificial Intelligence. [141] J. McCarthy, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon, "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence," August 31, 1955, accessed August 1, 2016, http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. [142] Thomas Bayes, “An Essay towards Solving a Problem in the Doctrine of Chances,” Philosophical Transactions of the Royal Society of London 53 (January 1, 1763): 370-418, accessed August 1, 2016, http://rstl.royalsocietypublishing.org/search?fulltext=an+essay+towards+solving&submit=yes&andorexactfulltext=and&x=0&y=0. [143] George Boole, An Investigation of the Laws of Thought on Which are Founded the Mathematical Theories of Logic and Probabilities, (Macmillan, 1854, reprinted with corrections, Dover Publications, New York, NY, 1958, and reissued by Cambridge University Press, 2009), accessed August 1, 2016, http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511693090. [144] “History of statistics,” Wikipedia, Last modified June 3, 2016, accessed August 1, 2016, https://en.wikipedia.org/wiki/History_of_statistics. [145] Joel N. Shurkin, Engines of the Mind: The Evolution of the Computer from Mainframes to Microprocessors (New York: W. W. Norton & Company, 1996). [146] William Grey Walter, “An Electromechanical Animal,” Dialectica 4 (1950): 42—49. [147] A. M. Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (1950): 433-460. [148] Marvin Minsky, “Steps toward Artificial Intelligence,” MIT Media Laboratory, October 24, 1960, accessed August 1, 2016, http://web.media.mit.edu/~minsky/papers/steps.html. [149] Allen Newell, John C Shaw, and Herbert A Simon, “Report on a general problem-solving program,” Proceedings of the International Conference on Information Processing, UNESCO, Paris 15-20 June 1959 (Unesco/Oldenbourg/Butterworths, 1960), 256-264. [150] O. G. Selfridge, “Pandemonium: A paradigm for learning,” Proceedings of the Symposium on Mechanization of Thought Processes (London: H. M. Stationary Office, 1959): 511-531. [151] Woodrow W. Bledsoe and Helen Chan, “A Man-Machine Facial Recognition System: Some Preliminary Results,” Technical Report PRI 19A (Palo Alto, California: Panoramic Research, Inc., 1965). [152] D. Raj Reddy, “Speech Recognition by Machine: A Review,” Proceedings of the IEEE 64, no.4 (April 1976), 501-531. [153] Arthur Samuel, “Some Studies in Machine Learning Using the Game of Checkers, IBM Journal of Research and Development 3, no. 3 (1959): 210—229. [154] Frank Rosenblatt, “The Perceptron—A Perceiving and Recognizing Automaton,” Report 85-460-1, (Buffalo, New York: Cornell Aeronautical Laboratory, 1957). [155] “Shakey the robot,” Wikipedia, last modified July 11, 2016, accessed August 1, 2016, https://en.wikipedia.org/wiki/Shakey_the_robot. [156] Edward A. Feigenbaum and Bruce G. Buchanan, “DENDRAL and Meta-DENDRAL: Roots of Knowledge Systems and Expert System Applications,” Artificial Intelligence 59, no. 1-2 (1993), 233-240. [157] John Haugeland, Artificial Intelligence: The Very Idea, (Cambridge, Massachusetts: MIT Press, 1985). [158] Rodney A. Brooks, “Elephants Don't Play Chess,” Robotics and Autonomous Systems 6, no. 1-2 (June 1990): 3-15. [159] David A. Ferrucci, “Introduction to ‘This is Watson,’” IBM Journal of Research and Development, 56, no. 3-4 (2012): 1. [160] David Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search.” [161] Steven Borowiec and Tracey Lien, "AlphaGo beats human Go champ in milestone for artificial intelligence," Los Angeles Times, March 12, 2016, accessed August 1, 2016, http://www.latimes.com/world/asia/la-fg-korea-alphago-20160312-story.html. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_352
Defining AI Curiously, the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace. Practitioners, researchers, and developers of AI are instead guided by a rough sense of direction and an imperative to “get on with it.” Still, a definition remains important and Nils J. Nilsson has provided a useful one: “Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.”[3] From this perspective, characterizing AI depends on the credit one is willing to give synthesized software and hardware for functioning “appropriately” and with “foresight.” A simple electronic calculator performs calculations much faster than the human brain, and almost never makes a mistake.[4] Is a calculator intelligent? Like Nilsson, the Study Panel takes a broad view that intelligence lies on a multi-dimensional spectrum. According to this view, the difference between an arithmetic calculator and a human brain is not one of kind, but of scale, speed, degree of autonomy, and generality. The same factors can be used to evaluate every other instance of intelligence—speech recognition software, animal brains, cruise-control systems in cars, Go-playing programs, thermostats—and to place them at some appropriate location in the spectrum. Although our broad interpretation places the calculator within the intelligence spectrum, such simple devices bear little resemblance to today’s AI. The frontier of AI has moved far ahead and functions of the calculator are only one among the millions that today's smartphones can perform. AI developers now work on improving, generalizing, and scaling up the intelligence currently found on smartphones. In fact, the field of AI is a continual endeavor to push forward the frontier of machine intelligence. Ironically, AI suffers the perennial fate of losing claim to its acquisitions, which eventually and inevitably get pulled inside the frontier, a repeating pattern known as the “AI effect” or the “odd paradox”—AI brings a new technology into the common fold, people become accustomed to this technology, it stops being considered AI, and newer technology emerges.[5] The same pattern will continue in the future. AI does not “deliver” a life-changing product as a bolt from the blue. Rather, AI technologies continue to get better in a continual, incremental way. [3] Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements (Cambridge, UK: Cambridge University Press, 2010). [4] Wikimedia Images, accessed August 1, 2016, https://upload.wikimedia.org/wikipedia/commons/b/b6/SHARP_ELSIMATE_EL-W221.jpg. [5] Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd ed. (Natick, MA: A. K. Peters, Ltd., 2004; San Francisco: W. H. Freeman, 1979), Citations are to the Peters edition. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_353
Section I: What is Artificial Intelligence? (Annotated) This section describes how researchers and practitioners define “Artificial Intelligence,” and the areas of AI research and application that are currently thriving. It proffers definitions of what AI is and is not, and describes some of the currently “hot” areas of AI Research. This section lays the groundwork for Section II, which elaborates on AI’s impacts and future in eight domains and Section III, which describes issues related to AI design and public policy and makes recommendations for encouraging AI innovation while protecting democratic values. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_354
Challenges and Opportunities (Annotated) [go back to the original version] One might have expected more and more sophisticated use of AI technologies in schools, colleges, and universities by now. Much of its absence can be explained by the lack of financial resources of local school systems, colleges, and universities as well as the lack of data establishing the technologies’ effectiveness. These problems are being addressed, albeit slowly, by private foundations and by numerous programs to train primarily secondary school teachers in summer programs. As in other areas of AI, excessive hype and promises about the capabilities of MOOCs have meant that expectations frequently exceed the reality. The experiences of certain institutions, such as San Jose State University’s experiment with Udacity,[91] have led to more sober assessment of the potential of the new educational technologies. In the next fifteen years, it is likely that human teachers will be assisted by AI technologies with better human interaction, both in the classroom and in the home. The Study Panel expects that more general and more sophisticated virtual reality scenarios in which students can immerse themselves in subjects from all disciplines will be developed. Some steps in this direction are being taken now by increasing collaborations between AI researchers and researchers in the humanities and social sciences, exemplified by Stanford’s Galileo Correspondence Project[92] and Columbia’s Making and Knowing Project.[93] These interdisciplinary efforts create interactive experiences with historical documents and the use of Virtual Reality (VR) to explore interactive archeological sites.[94] VR techniques are already being used in the natural sciences such as biology, anatomy, geology and astronomy to allow students to interact with environments and objects that are difficult to engage with in the real world. The recreation of past worlds and fictional worlds will become just as popular for studies of arts and other sciences. AI techniques will increasingly blur the line between formal, classroom education and self-paced, individual learning. Adaptive learning systems, for example, are going to become a core part of the teaching process in higher education because of the pressures to contain cost while serving a larger number of students and moving students through school more quickly. While formal education will not disappear, the Study Panel believes that MOOCs and other forms of online education will become part of learning at all levels, from K-12 through university, in a blended classroom experience. This development will facilitate more customizable approaches to learning, in which students can learn at their own pace using educational techniques that work best for them. Online education systems will learn as the students learn, supporting rapid advances in our understanding of the learning process. Learning analytics, in turn, will accelerate the development of tools for personalized education. The current transition from hard copy books to digital and audio media and texts is likely to become prevalent in education as well. Digital reading devices will also become much ‘smarter’, providing students with easy access to additional information about subject matter as they study. Machine Translation (MT) technology will also make it easier to translate educational material into different languages with a fair degree of accuracy, just as it currently translates technical manuals. Textbook translation services that currently depend only upon human translators will increasingly incorporate automatic methods to improve the speed and affordability of their services for school systems. Online learning systems will also expand the opportunity for adults and working professionals to enhance their knowledge and skills (or to retool and learn a new field) in a world where these fields are evolving rapidly. This will include the expansion of fully online professional degrees as well as professional certifications based on online coursework. [91] Ry Rivard, "Udacity Project on 'Pause'," Inside Higher Ed, July 18, 2013, accessed August 1, 2016, https://www.insidehighered.com/news/2013/07/18/citing-disappointing-student-outcomes-san-jose-state-pauses-work-udacity. [92] Stanford University: Galileo Correspondence Project, accessed August 1, 2016, http://galileo.stanford.edu. [93] The Making and Knowing Project: Reconstructing the 16th Century Workshop of BNF MS. FR. 640 at Columbia University, accessed August 1, 2016, http://www.makingandknowing.org. [94] Paul James, “3D Mapped HTC Vive Demo Brings Archaeology to Life,” Road to VR, August 31, 2015, accessed August 1, 2016, http://www.roadtovr.com/3d-mapped-htc-vive-demo-brings-archaeology-to-life/. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_355
Intelligent Tutoring Systems and Online Learning ITS have been developed from research laboratory projects such as Why-2 Atlas,[79] which supported human-machine dialogue to solve physics problems early in the era. The rapid migration of ITS from laboratory experimental stages to real use is surprising and welcome. Downloadable software and online systems such as Carnegie Speech or Duolingo provide foreign language training using Automatic Speech Recognition (ASR) and NLP techniques to recognize language errors and help users correct them.[80] Tutoring systems such as the Carnegie Cognitive Tutor[81] have been used in US high schools to help students learn mathematics. Other ITS have been developed for training in geography, circuits, medical diagnosis, computer literacy and programming, genetics, and chemistry. Cognitive tutors use software to mimic the role of a good human tutor by, for example, providing hints when a student gets stuck on a math problem. Based on the hint requested and the answer provided, the tutor offers context specific feedback. Applications are growing in higher education. An ITS called SHERLOCK[82] is beginning to be used to teach Air Force technicians to diagnose electrical systems problems in aircraft. And the University of Southern California’s Information Sciences Institute has developed more advanced avatar-based training modules to train military personnel being sent to international posts in appropriate behavior when dealing with people from different cultural backgrounds. New algorithms for personalized tutoring, such as Bayesian Knowledge Tracing, enable individualized mastery learning and problem sequencing.[83] Most surprising has been the explosion of the Massive Open Online Courses (MOOCs) and other models of online education at all levels—including the use of tools like Wikipedia and Khan Academy as well as sophisticated learning management systems that build in synchronous as well as asynchronous education and adaptive learning tools. Since the late 1990s, companies such as the Educational Testing Service and Pearson have been developing automatic NLP assessment tools to co-grade essays in standardized testing.[84] Many of the MOOCs which have become so popular, including those created by EdX, Coursera, and Udacity, are making use of NLP, machine learning, and crowdsourcing techniques for grading short-answer and essay questions as well as programming assignments.[85] Online education systems that support graduate-level professional education and lifelong learning are also expanding rapidly. These systems have great promise because the need for face-to-face interaction is less important for working professionals and career changers. While not the leaders in AI-supported systems and applications, they will become early adopters as the technologies are tested and validated. It can be argued that AI is the secret sauce that has enabled instructors, particularly in higher education, to multiply the size of their classrooms by a few orders of magnitude—class sizes of a few tens of thousands are not uncommon. In order to continually test large classes of students, automated generation of the questions is also possible, such as those designed to assess vocabulary,[86] wh (who/what/when/where/why) questions,[87] and multiple choice questions,[88] using electronic resources such as WordNet, Wikipedia, and online ontologies. With the explosion of online courses, these techniques are sure to be eagerly adopted for use in online education. Although the long term impact of these systems will have on the educational system remains unclear, the AI community has learned a great deal in a very short time. [79] Kurt VanLehn, Pamela W. Jordan, Carolyn P. Rosé, Dumisizwe Bhembe, Michael Böttner, Andy Gaydos, Maxim Makatchev, Umarani Pappuswamy, Michael Ringenberg, Antonio Roque, Stephanie Siler, and Ramesh Srivastava, “The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing,” Intelligent Tutoring Systems: Proceedings of the 6th International Conference, (Springer Berlin Heidelberg, 2002), 158-167. [80] VanLehn et al, “The Architecture of Why2-Atlas.” [81] “Resources and Support,” Carnegie Learning, accessed August 1, 2016, https://www.carnegielearning.com/resources-support/. [82] Alan Lesgold, Suzanne Lajoie, Marilyn Bunzo, and Gary Eggan, “SHERLOCK: A Coached Practice Environment for an Electronics Troubleshooting Job,” in J. H. Larkin and R. W. Chabay, eds., Computer-Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and Complementary Approaches (Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1988). [83] Michael V. Yudelson, Kenneth R. Koedinger, and Geoffrey J. Gordon, (2013). " Individualized Bayesian Knowledge Tracing Models," Artificial Intelligence in Education, (Springer Berlin Heidelberg, 2013), 171-180. [84] Jill Burstein, Karen Kukich, Susanne Wolff, Chi Lu, Martin Chodorow, Lisa Braden-Harder, and Mary Dee Harris, “Automated Scoring Using a Hybrid Feature Identification Technique” in Proceedings of the Annual Meeting of the Association of Computational Linguistics, Montreal, Canada, August 1998, accessed August 1, 2016, https://www.ets.org/Media/Research/pdf/erater_acl98.pdf. [85] EdX, https://www.edx.org/, Coursera, https://www.coursera.org/, Udacity, https://www.udacity.com/, all accessed August 1, 2016. [86] Jonathan C. Brown, Gwen A. Frishkoff , and Maxine Eskenazi, “Automatic Question Generation for Vocabulary Assessment,” Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, October 2005, (Association for Computational Linguistics, 2005), 819–826. [87] Michael Heilman, “Automatic Factual Question Generation from Text,” PhD thesis CMU-LTI-11-004, (Carnegie Mellon University, 2011), accessed August 1, 2016, http://www.cs.cmu.edu/~ark/mheilman/questions/papers/heilman-question-generation-dissertation.pdf. [88] Tahani Alsubait, Bijan Parsia, and Uli Sattler, “Generating Multiple Choice Questions from Ontologies: How Far Can We Go?,” in eds. P. Lambrix, E. Hyvönen. E. Blomqvist, V. Presutti, G. Qi, U. Sattler, Y. Ding, and C. Ghidini, Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic Linköping, Sweden, November 24–28, 2014 Revised Selected Papers, (Switzerland: Springer International Publishing, 2015), 66-79. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_356
Elder Care Over the next fifteen years the number of elderly in the United States will grow by over 50%.[74] The National Bureau of Labor Statistics projects that home health aides will grow 38% over the next ten years. Despite the broad opportunities in this domain—basic social support, interaction and communication devices, home health monitoring, a variety of simple in-home physical aids such as walkers, and light meal preparation—little has happened over the past fifteen years. But the coming generational shift will accompany a change in technology acceptance among the elderly. Currently, someone who is seventy was born in 1946 and may have first experienced some form of personalized IT in middle age or later, while a fifty-year-old today is far more technology-friendly and savvy. As a result, there will be a growing interest and market for already available and maturing technologies to support physical, emotional, social, and mental health. Here are a few likely examples by category: Life quality and independence - Automated transportation will support continued independence and expanded social horizons. - Sharing of information will help families remain engaged with one another at a distance, and predictive analytics may be used to “nudge” family groups toward positive behaviors, such as reminders to “call home.” - Smart devices in the home will help with daily living activities when needed, such as cooking and, if robot manipulation capabilities improve sufficiently, dressing and toileting. Health and wellness - Mobile applications that monitor movement and activities, coupled with social platforms, will be able to make recommendations to maintain mental and physical health. - In-home health monitoring and health information access will be able to detect changes in mood or behavior and alert caregivers. - Personalized health management will help mitigate the complexities associated with multiple co-morbid conditions and/or treatment interactions. Treatments and devices - Better hearing aids and visual assistive devices will mitigate the effects of hearing and vision loss, improving safety and social connection. - Personalized rehabilitation and in-home therapy will reduce the need for hospital or care facility stays. - Physical assistive devices (intelligent walkers, wheel chairs, and exoskeletons) will extend the range of activities of an infirm individual. The Study Panel expects an explosion of low-cost sensing technologies that can provide substantial capabilities to the elderly in their homes. In principle, social agents with a physical presence and simple physical capabilities (e.g. a mobile robot with basic communication capabilities) could provide a platform for new innovations. However, doing so will require integration across multiple areas of AI—Natural Language Processing, reasoning, learning, perception, and robotics—to create a system that is useful and usable by the elderly. These innovations will also introduce questions regarding privacy within various circles, including friends, family, and care-givers, and create new challenges to accommodate an evermore active and engaged population far past retirement. [74] Jennifer M. Ortman, Victoria A. Velkoff, and Howard Hogan, "An Aging Nation: The Older Population in the United States: Population Estimates and Projections," Current Population Reports, U.S Census Bureau (May 2014), accessed August 1, 2016, https://www.census.gov/prod/2014pubs/p25-1140.pdf. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_357
Home Robots 2030 (Annotated) Despite the slow growth to date of robots in the home, there are signs that this will change in the next fifteen years. Corporations such as Amazon Robotics and Uber are developing large economies of scale using various aggregation technologies. Also: System in Module (SiM), with a lot of System on Chip (SoC) subsystems, are now being pushed out the door by phone-chip makers (Qualcomm’s SnapDragon, Samsung’s Artik, etc.). These are better than supercomputers of less than ten years ago with eight or more sixty-four-bit cores, and specialized silicon for cryptography, camera drivers, additional DSPs, and hard silicon for certain perceptual algorithms. This means that low cost devices will be able to support much more onboard AI than we have been able to consider over the last fifteen years. Cloud (“someone else’s computer”) is going to enable more rapid release of new software on home robots, and more sharing of data sets gathered in many different homes, which will in turn feed cloud-based machine learning, and then power improvements to already deployed robots. The great advances in speech understanding and image labeling enabled by deep learning will enhance robots’ interactions with people in their homes. Low cost 3D sensors, driven by gaming platforms, have fueled work on 3D perception algorithms by thousands of researchers worldwide, which will speed the development and adoption of home and service robots. In the past three years, low cost and safe robot arms have been introduced to hundreds of research labs around the world, sparking a new class of research on manipulation that will eventually be applicable in the home, perhaps around 2025. More than half a dozen startups around the world are developing AI-based robots for the home, for now concentrating mainly on social interaction. New ethics and privacy issues may surface as a result. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_358
Vacuum Cleaners (Annotated) In 2001, after many years of development, the Electrolux Trilobite, a vacuum cleaning robot, became the first commercial home robot. It had a simple control system to do obstacle avoidance, and some navigation. A year later, iRobot introduced Roomba, which was a tenth the price of the Trilobite and, with only 512 bytes of RAM, ran a behavior based controller. The most intelligent thing it did was to avoid falling down stairs. Since then, sixteen million Roombas have been deployed all over the world and several other competing brands now exist. As the processing power and RAM capacity of low cost embedded processors improved from its dismal state in the year 2000, the AI capabilities of these robots also improved dramatically. Simple navigation, self-charging, and actions for dealing with full dust bins were added, followed by ability to deal with electrical cords and rug tassels, enabled by a combination of mechanical improvements and sensor based perception. More recently, the addition of full VSLAM (Visual Simultaneous Location and Mapping)— an AI technology that had been around for twenty years—has enabled the robots to build a complete 3D world model of a house as they clean, and become more efficient in their cleaning coverage. Early expectations that many new applications would be found for home robots have not materialized. Robot vacuum cleaners are restricted to localized flat areas, while real homes have lots of single steps, and often staircases; there has been very little research on robot mobility inside real homes. Hardware platforms remain challenging to build, and there are few applications that people want enough to buy. Perceptual algorithms for functions such as image labeling, and 3D object recognition, while common at AI conferences, are still only a few years into development as products. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_359
Home/Service Robots (Annotated) [go back to the original version] Robots have entered people’s homes in the past fifteen years. Surprisingly slow growth in the diversity of applications has occurred simultaneously with increasingly sophisticated AI deployed on existing applications. AI advances are often inspired by mechanical innovations, which in turn prompt new AI techniques to be introduced. Over the next fifteen years, coincident advances in mechanical and AI technologies promise to increase the safe and reliable use and utility of home robots in a typical North American city. Special purpose robots will deliver packages, clean offices, and enhance security. But technical constraints and the high costs of reliable mechanical devices will continue to limit commercial opportunities to narrowly defined applications for the foreseeable future. As with self-driving cars and other new transportation machines, the difficulty of creating reliable, market-ready hardware is not to be underestimated. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_360
Home/Service Robots (Annotated) [go back to the original version] Robots have entered people’s homes in the past fifteen years. Surprisingly slow growth in the diversity of applications has occurred simultaneously with increasingly sophisticated AI deployed on existing applications. AI advances are often inspired by mechanical innovations, which in turn prompt new AI techniques to be introduced. Over the next fifteen years, coincident advances in mechanical and AI technologies promise to increase the safe and reliable use and utility of home robots in a typical North American city. Special purpose robots will deliver packages, clean offices, and enhance security. But technical constraints and the high costs of reliable mechanical devices will continue to limit commercial opportunities to narrowly defined applications for the foreseeable future. As with self-driving cars and other new transportation machines, the difficulty of creating reliable, market-ready hardware is not to be underestimated. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_361
Self-driving Vehicles (Annotated) [go back to the original version] Since the 1930s, science fiction writers dreamed of a future with self-driving cars, and building them has been a challenge for the AI community since the 1960s. By the 2000s, the dream of autonomous vehicles became a reality in the sea and sky, and even on Mars, but self-driving cars existed only as research prototypes in labs. Driving in a city was considered to be a problem too complex for automation due to factors like pedestrians, heavy traffic, and the many unexpected events that can happen outside of the car’s control. Although the technological components required to make such autonomous driving possible were available in 2000—and indeed some autonomous car prototypes existed[30] [31] [32]—few predicted that mainstream companies would be developing and deploying autonomous cars by 2015. During the first Defense Advanced Research Projects Agency (DARPA) “grand challenge” on autonomous driving in 2004, research teams failed to complete the challenge in a limited desert setting. But in eight short years, from 2004-2012, speedy and surprising progress occurred in both academia and industry. Advances in sensing technology and machine learning for perception tasks has sped progress and, as a result, Google’s autonomous vehicles and Tesla’s semi-autonomous cars are driving on city streets today. Google’s self-driving cars, which have logged more than 1,500,000 miles (300,000 miles without an accident),[33] are completely autonomous—no human input needed. Tesla has widely released self-driving capability to existing cars with a software update.[34] Their cars are semi-autonomous, with human drivers expected to stay engaged and take over if they detect a potential problem. It is not yet clear whether this semi-autonomous approach is sustainable, since as people become more confident in the cars' capabilities, they are likely to pay less attention to the road, and become less reliable when they are most needed. The first traffic fatality involving an autonomous car, which occurred in June of 2016, brought this question into sharper focus.[35] In the near future, sensing algorithms will achieve super-human performance for capabilities required for driving. Automated perception, including vision, is already near or at human-level performance for well-defined tasks such as recognition and tracking. Advances in perception will be followed by algorithmic improvements in higher level reasoning capabilities such as planning. A recent report predicts self-driving cars to be widely adopted by 2020.[36] And the adoption of self-driving capabilities won’t be limited to personal transportation. We will see self-driving and remotely controlled delivery vehicles, flying vehicles, and trucks. Peer-to-peer transportation services (e.g. ridesharing) are also likely to utilize self-driving vehicles. Beyond self-driving cars, advances in robotics will facilitate the creation and adoption of other types of autonomous vehicles, including robots and drones. It is not yet clear how much better self-driving cars need to become to encourage broad acceptance. The collaboration required in semi-self-driving cars and its implications for the cognitive load of human drivers is not well understood. But if future self-driving cars are adopted with the predicted speed, and they exceed human-level performance in driving, other significant societal changes will follow. Self-driving cars will eliminate one of the biggest causes of accidental death and injury in United States, and lengthen people’s life expectancy. On average, a commuter in US spends twenty-five minutes driving each way.[37] With self-driving car technology, people will have more time to work or entertain themselves during their commutes. And the increased comfort and decreased cognitive load with self-driving cars and shared transportation may affect where people choose to live. The reduced need for parking may affect the way cities and public spaces are designed. Self-driving cars may also serve to increase the freedom and mobility of different subgroups of the population, including youth, elderly and disabled. Self-driving cars and peer-to-peer transportation services may eliminate the need to own a vehicle. The effect on total car use is hard to predict. Trips of empty vehicles and people’s increased willingness to travel may lead to more total miles driven. Alternatively, shared autonomous vehicles—people using cars as a service rather than owning their own—may reduce total miles, especially if combined with well-constructed incentives, such as tolls or discounts, to spread out travel demand, share trips, and reduce congestion. The availability of shared transportation may displace the need for public transportation—or public transportation may change form towards personal rapid transit, already available in four cities,[38] which uses small capacity vehicles to transport people on demand and point-to-point between many stations.[39] As autonomous vehicles become more widespread, questions will arise over their security, including how to ensure that technologies are safe and properly tested under different road conditions prior to their release. Autonomous vehicles and the connected transportation infrastructure will create a new venue for hackers to exploit vulnerabilities to attack. There are also ethical questions involved in programming cars to act in situations in which human injury or death is inevitable, especially when there are split-second choices to be made regarding whom to put at risk. The legal systems in most states in the US do not have rules covering self-driving cars. As of 2016, four states in the US (Nevada, Florida, California, and Michigan), Ontario in Canada, the United Kingdom, France, and Switzerland have passed rules for the testing of self-driving cars on public roads. Even these laws do not address issues about responsibility and assignment of blame for an accident for self-driving and semi-self-driving cars.[40] [30] "Navlab," Wikipedia, last updated June 4, 2016, accessed August 1, 2016, https://en.wikipedia.org/wiki/Navlab. [31] "Navlab: The Carnegie Mellon University Navigation Laboratory," Carnegie Mellon University, accessed August 1, 2016, http://www.cs.cmu.edu/afs/cs/project/alv/www/. [32] "Eureka Prometheus Project," Wikipedia, last modified February 12, 2016, accessed August 1, 2016, https://en.wikipedia.org/wiki/Eureka_Prometheus_Project. [33] “Google Self-Driving Car Project,” Google, accessed August 1, 2016, https://www.google.com/selfdrivingcar/. [34] Molly McHugh, "Tesla’s Cars Now Drive Themselves, Kinda," Wired, October 14, 2015, accessed August 1, 2016, http://www.wired.com/2015/10/tesla-self-driving-over-air-update-live/. [35] Anjali Singhvi and Karl Russell, "Inside the Self-Driving Tesla Fatal Accident," The New York Times, Last updated July 12, 2016, accessed August 1, 2016, http://www.nytimes.com/interactive/2016/07/01/business/inside-tesla-accident.html. [36] John Greenough, "10 million self-driving cars will be on the road by 2020," Business Insider, June 15, 2016, accessed August 1, 2016, http://www.businessinsider.com/report-10-million-self-driving-cars-will-be-on-the-road-by-2020-2015-5-6. [37] Brian McKenzie and Melanie Rapino, "Commuting in the United States: 2009," American Community Survey Reports, United States Census Bureau, September 2011, accessed August 1, 2016, https://www.census.gov/prod/2011pubs/acs-15.pdf. [38] Morgantown, West Virginia; Masdar City, UAE; London, England; and Suncheon, South Korea. [39] "Personal rapid transit," Wikipedia, Last modified July 18, 2016, accessed August 1, 2016, https://en.wikipedia.org/wiki/Personal_rapid_transit. [40] Patrick Lin, "The Ethics of Autonomous Cars," The Atlantic, October 8, 2013, accessed August 1, 2016, http://www.theatlantic.com/technology/archive/2013/10/the-ethics-of-autonomous-cars/280360/. Cite This Report Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016. Report Authors AI100 Standing Committee and Study Panel Copyright © 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_362
SQ4. How much have we progressed in understanding the key mysteries of human intelligence? AI, the study of how to build an intelligent machine, and cognitive science, the study of human intelligence, have been evolving in complementary ways. A view of human intelligence that has gained prominence over the last five years holds that it is collective—that individuals are just one cog in a larger intellectual machine. Their roles in that collective are likely to be different than the roles of machines, because their strengths are different. Humans are able to share intentionality with other humans—to pursue common goals as a team—and doing so may require having features that are uniquely human: a certain kind of biological hardware with its associated needs and a certain kind of cultural experience. In contrast, machines have vast storehouses of data, along with the ability to process it and communicate with other machines at remarkable speeds. In that sense, AI is developing in ways that improve its ability to collaborate with and support people, rather than in ways that mimic human intelligence. Still, there are remarkable parallels between the operation of individual human minds and that of deep learning machines. AI seems to be evolving in a way that adopts some core human features—specifically, those that relate to perception and memory. In the early days of AI, a traditional view of human intelligence understood it as a distinct capacity of human cognition, related to a person’s ability to process information. Intelligence was a property that could be measured in any sufficiently complex cognitive system, and individuals differed in their mental horsepower. Today, this view is rare among cognitive scientists and others who study human intelligence. The key mysteries of human intelligence that have come to concern researchers for more than a decade include questions not only about how people are able to interpret complex inputs, solve difficult problems, and make reasonable judgments and decisions quickly, but also how we are able to negotiate emotionally nuanced relationships, use attitudes and emotions and other bodily signals to guide our decision-making, and understand other people’s intentions.1 The study of intelligence has become the study of how people are able to adapt and succeed, not just how an impressive information-processing system works. The modern study of human intelligence draws on a variety of forms of evidence. Cognitive psychology uses experimental studies of cognitive performance to look at the nature of human cognition and its capabilities. Collective intelligence is the study of how intelligence is designed for and emerges from group (rather than individual) activity. Psychometrics is the study of how people vary in what they can do, how their capabilities are determined, and how abilities relate to demographic variables. Cognitive neuroscience looks at how the brain’s hardware is involved in implementing psychological and social processes. In the context of cognitive science, artificial intelligence is concerned with how advances in automating skills associated with humans provide proofs-of-concept about how humans might go about doing the same things. Developments in human-intelligence research in the last five years have been inspired more by collective intelligence,2 cognitive neuroscience,3 and artificial intelligence4 than by cognitive psychology or psychometrics. The study of working memory, attention, and executive processing in cognitive psychology, once understood as the mental components supporting intelligence, have become central topics in the study of cognitive neuroscience.5 Psychometric work on intelligence itself has splintered, due to the recognition that a single “intelligence” dimension like IQ does not adequately characterize human problem-solving potential.6 Abilities like empathy, impulse control, and storytelling turn out to be just as important. Over the past half decade, major shifts in the understanding of human intelligence have favored the topics discussed below. Collective Intelligence Research from a variety of fields reinforces the view that intelligence is a property not only of individuals, but also of collectives.7 As we know from work on the wisdom of crowds,8 collectives can be surprisingly insightful, especially when many individuals with relevant knowledge make independent contributions, unaffected by pressures to conform to group norms. Deliberating groups can also exhibit greater intelligence than individuals, especially when they follow norms that encourage challenge and constructive criticism. Intelligence is a group property in the sense that the quality of a group’s performance does not depend on the IQs of the individual members of the group.9 It is easier to predict group performance if you know how good the group is at turn-taking or how empathetic the members are than if you know the IQs of group members. Research on children shows they are sensitive to what others know when deciding whose advice to take.10 Studies of social networks have shown the role of collective intelligence in determining individual beliefs. Some of those studies help explain the distribution of beliefs across society, showing that patterns of message transmission in social networks can account for both broad acceptance of beliefs endorsed by science and simultaneous minority acceptance of conspiracy theories.11 Such studies also offer a window into political polarization by showing that even a collection of rational decision-makers can end up splitting into incompatible subgroups under the influence of information bubbles.12 In the most general sense, the research community is starting to see the mind as a collective entity spread across members of a group. People obviously have skills that they engage in as individuals, but the majority of knowledge that allows them to operate day by day sits in the heads of other members of their community.13 Our sense of understanding is affected by what others know, and we rely on others for the arguments that constitute our explanations, often without knowing that we are doing so. For instance, we might believe we understand the motivation for a health policy (wear a mask in public!) but actually we rely on experts or the internet to spell it out. We suppose we understand how everyday objects like toilets work—and discover our ignorance when we try to explain their mechanism,14 or when they break. At a broader level, our communities determine our political and social beliefs and attitudes. Political partisanship influences many beliefs and actions,15 some that have nothing to do with politics,16 even some related to life and death.17 Cognitive Neuroscience Work in cognitive neuroscience has started to productively examine a variety of higher-level skills associated with the more traditional view of intelligence. Three partially competing ideas have reached some consensus over the last few years. First, a pillar of cognitive neuroscience is that properties of individuals such as working memory and executive control are central to domain-independent intelligence, that which governs performance on all cognitive tasks regardless of their mode or topic. A common view is that this sort of intelligence is governed by neural speed.18 But there is increasing recognition that what matters is not global neural speed per se, but the efficiency of higher-order processing. Efficiency is influenced not just by speed, but by how processing is organized.19 A second idea gaining support is that higher-ability individuals are characterized by more efficient patterns of brain connectivity. Both of these ideas are consistent with the dominant view that intelligence is associated with higher-level brain areas in the parieto-frontal cortex. The third idea is more radical. It suggests that the neural correlates of intelligence are distributed throughout the brain.20 In this view, the paramount feature of human intelligence is flexibility, the ability to continually update prior knowledge and to generate predictions. Intelligence derives from the brain’s ability to dynamically generate inferences that anticipate sensory inputs. This flexibility is realized as brain plasticity—the ability to change—housed in neural connections that exhibit what network scientists call a “small-world” pattern, where the brain balances relatively focal, densely interconnected, functional centers with long-range connections that allow for more global integration of information. Cognitive neuroscience has taken a step in the direction of collective cognition via a sub-discipline called social neuroscience. Its motivation is the recognition that one of the brain’s unique and most important capacities is its ability to grasp what others are thinking and feeling. The field has thus focused on issues that are old stalwarts of social psychology—fairness, punishment, and people’s tendency to cooperate versus compete—and on identifying hormones and brain networks that are involved in these activities. Unlike other branches of cognitive neuroscience, social neuroscience recognizes that human cognitive, emotional, and behavioral processes have been shaped by our social environments. A corollary of developments in cognitive neuroscience is the growth of the related field of computational neuroscience, which brings a computational perspective to the investigation of brains and minds. This field has been aided tremendously by the machine-learning paradigm known as reinforcement learning, which is concerned with learning from evaluative feedback—reward and punishment.21 It has proven to be a goldmine of ideas for understanding learning in the brain, since each element of the computational theory can be linked to processes at the cellular level. For instance, there is now broad consensus about the central role of the dopamine system in learning, decision-making, motivation, prediction, motor control, habit, and addiction.22 Computational Modeling For decades now, trends in computational modeling of cognition have followed a recurring pattern, cycling between a primary focus on logic (symbolic reasoning) and on pattern recognition (neural networks). In the past five to 10 years, neural net models have been in the spotlight—due in small part to the success of computational neuroscience and in large part to the success of deep learning in AI. The computational modeling field is now full of deep-learning-inspired models of visual recognition, language processing, and other cognitive activities. There remains a fair amount of excitement about Bayesian modeling—a type of logic infused with probabilities. But the clash with deep learning techniques has stirred a heated debate. Is it better to make highly accurate predictions without understanding exactly why, or better to make less accurate predictions but with a clear logic behind them?23 We expect this debate will be further explored in future AI100 reports. Beyond efforts to build computational models, deep learning models have become central methodological weapons in the cognitive science arsenal. They are the state-of-the-art tools for classification, helping experimentalists to quickly construct large stimulus sets for experiments and analysis. Moreover, huge networks trained on enormous quantities of data, such as GPT-3 and Grover, have opened new territory for the study of language and discourse at multiple levels. The State of the Art The nature of consciousness remains an open question. Some see progress;24 others believe we are no further along in understanding how to build a conscious agent than we were 46 years ago, when the philosopher Thomas Nagel famously posed the question, “What is it like to be a bat?”25 It is not even clear that understanding consciousness is necessary for understanding human intelligence. The question has become less pressing for this purpose as we have begun to recognize the limits of conscious information processing in human cognition,26 and as our models become increasingly based on emergent processes instead of central design. Cognitive models motivate an analysis of how people integrate information from multiple modalities, multiple senses, and multiple sources: our brains, our physical bodies, physical objects (pen, paper, computers), and social entities (other people, Wikipedia). Although there is now a lot of evidence that it is the ability to do this integration that supports humanity’s more remarkable achievements, how we do so remains largely mysterious. Relatedly, there is increased recognition of the importance of processes that support intentional action, shared intentionality, free will, and agency. But there has been little fundamental progress on building rigorous models of these processes. The cognitive sciences continue to search for a paradigm for studying human intelligence that will endure. Still, the search is uncovering critical perspectives—like collective cognition—and methodologies that will shape future progress, like cognitive neuroscience and the latest trends in computational modeling. These insights seem essential in our quest for building machines that we would truly judge as “intelligent.” [1] Robert J. Sternberg and Scott Barry Kaufman (Eds.), The Cambridge Handbook of Intelligence, Cambridge University Press, 2011. [2] Thomas W. Malone and Michael S. Bernstein (Eds.), Handbook of Collective Intelligence. MIT Press, 2015. [3] Aron K. Barbey, Sherif Karama, and Richard J. Haier (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience, Cambridge University Press, 2021. [4] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep learning,” Nature, issue 521, pages 436–444: May 28, 2015 https://www.nature.com/articles/nature14539 [5] Mark D'Esposito and Bradley R. Postle, “The Cognitive Neuroscience of Working Memory,” Annual Review of Psychology, Vol. 66, pages 115–142 https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010814-015031 [6] Robert J. Sternberg and Scott Barry Kaufman (Eds.), The Cambridge Handbook of Intelligence, Cambridge University Press, 2011. [7] Steven Sloman and Philip Fernbach, The Knowledge Illusion, Riverhead Books, 2018. [8] Albert E. Mannes, Richard. P. Larrick, and Jack B. Soll, “The social psychology of the wisdom of crowds,” in J. I. Krueger (Ed.), Social judgment and decision making, Psychology Press, 2012. https://psycnet.apa.org/record/2011-26824-013 [9] Anita Williams Woolley, Christopher F. Chabris, Alex Pentland, Nada Hashmi, And Thomas W. Malone, “Evidence for a Collective Intelligence Factor in the Performance of Human Groups,” Science, Vol. 330, Issue 6004, pages 686–688: Oct. 29, 2010. https://science.sciencemag.org/content/330/6004/686 [10] Cecilia Heyes, “Who Knows? Metacognitive Social Learning Strategies,” Trends in Cognitive Sciences, Volume 20, Issue 3, Pages 204-213: March 2016. https://www.sciencedirect.com/science/article/pii/S1364661315003125 [11] Cailin O'Connor and James Owen Weatherall, The Misinformation Age, Yale University Press, 2019. [12] Jens Koed Madsen, Richard M. Bailey, and Toby D. Pilditch, “Large networks of rational agents form persistent echo chambers,” Scientific Reports 8, Article Number 12391, 2018. https://www.nature.com/articles/s41598-018-25558-7 [13] Steven Sloman and Philip Fernbach, The Knowledge Illusion, Riverhead Books, 2018. [14] Leonid Rozenblit, and Frank Keil. “The Misunderstood Limits of Folk Science: An Illusion of Explanatory Depth,” Cognitive Science, Vol. 26 (5), Pages 521–562, 2002. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062901/ [15] Phillip J. Ehret, Leaf Van Boven, and David K. Sherman, “Partisan Barriers to Bipartisanship: Understanding Climate Policy Polarization,” Social Psychological and Personality Science, Volume 9, Issue 3, Pages 308–318: April 2018. https://journals.sagepub.com/doi/full/10.1177/1948550618758709 [16] Joseph Marks, Eloise Copland, Eleanor Loh, Cass R. Sunstein, and Tali Sharot, “Epistemic spillovers: Learning others’ political views reduces the ability to assess and use their expertise in nonpolitical domains,” Cognition, Volume 188, Pages 74–84: July 2019. https://www.sciencedirect.com/science/article/pii/S0010027718302609 [17] Mae K. Fullerton, Nathaniel Rabb, Sahit Mamidipaka, Lyle Ungar, Steven A. Sloman, “Evidence against risk as a motivating driver of COVID-19 preventive behaviors in the United States,” Journal of Health Psychology, June 2021. https://journals.sagepub.com/doi/10.1177/13591053211024726 [18] Anna-Lena Schubert, Dirk Hagemann, and Gidon T. Frischkorn, “Is General Intelligence Little More Than The Speed Of Higher-order Processing?” Journal of Experimental Psychology: General, 146 (10), Pages 1498–1512, 2017. https://psycnet.apa.org/record/2017-30267-001 [19] Ibid. [20] Aron K. Barbey, “Network Neuroscience Theory of Human Intelligence,” Trends in Cognitive Sciences, 22(1), Pages 8–20, 2018. https://psycnet.apa.org/record/2017-57554-004 [21] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd Ed., MIT Press, 2018. http://incompleteideas.net/book/the-book.html [22] Maria K. Eckstein, Linda Wilbrecht, and Anne GE Collins, “What Do Reinforcement Learning Models Measure? Interpreting Model Parameters In Cognition And Neuroscience,” Current Opinion in Behavioral Sciences, Volume 41, Pages 128–137, October 2021. https://www.sciencedirect.com/science/article/pii/S2352154621001236 [23] Brendan Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman, “Building Machines That Learn And Think Like People,” Behavioral and Brain Sciences, 40, E253, 2017. https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/building-machines-that-learn-and-think-like-people/A9535B1D745A0377E16C590E14B94993 [24] Giulio Tononi, Melanie Boly, Marcello Massimini, and Christof Koch, “Integrated Information Theory: From Consciousness To Its Physical Substrate,” Nature Reviews Neuroscience 17, Pages 450–461, 2016. https://www.nature.com/articles/nrn-2016-44 [25] Michael A. Cerullo, “The Problem with Phi: A Critique of Integrated Information Theory,” PLoS Computational Biology 11 (9): September 2015. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004286 [26] Matthieu Raoelison, Esther Boissin, Grégoire Borst, and Wim De Neys, “From Slow To Fast Logic: The Development Of Logical Intuitions,” Thinking & Reasoning, 2021. doi.org/10.1080/13546783.2021.1885488 Cite This Report Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, and Toby Walsh. "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report." Stanford University, Stanford, CA, September 2021. Doc: http://ai100.stanford.edu/2021-report. Accessed: September 16, 2021. Report Authors AI100 Standing Committee and Study Panel Copyright © 2021 by Stanford University. Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International): https://creativecommons.org/licenses/by-nd/4.0/.	common_crawl_stanford.edu_363
In this 2nd AI4AEC Colloquium we will explore how a range of AI methods can provide the understanding necessary to create and improve models of the physical and temporal interactions across different scales to support AEC professionals in reusing buildings. Our exploration will take place in several online sessions on several dates that focus on building and space reuse and repurposing at the building, urban, land scales. In each session, three invited speakers from different backgrounds will present their perspective on the matter, followed by a discussion. The session will end with a leading expert keynote speaker. Session 1 focused on the building scale and Session 2 on building reuse in urban contexts; Session 3 will focus on building and infrastructure reuse in regional contexts with Stefan Holy, the Deputy Prime Minister of the Slovak Republic, being the keynote speaker. The AEC industry creates the built environment that shapes much of our lives. Surprisingly, the industry uses mostly static and deterministic models to design, build, and operate the built environment even though the built environment and our interactions with it change over time. It has been out of reach to understand the changes of this socio-technical system over time given the physical and temporal scales and the many types of interactions, leaving the industry with static models to envision and create our future. This lack of functional, economic, environmental, and social performance models limits our ability to shape the sustainability of the built environment, especially under the light of circularity. Building reuse is a key sustainability strategy because it offers the shortest cycle in achieving a circular built environment and avoids longer cycles of material recycling or, even worse, the building ending up in a landfill after it is no longer needed for its initial use. Assessing a building's reusability and envisioning its future requires an understanding of the building's complex physical and temporal scales and internal and external interactions. Creating this understanding is error-prone and time-consuming today. For example, at the building scale, the functional fit between the previous and next use, the structural capacity at the material and system levels, and the ease of adaptation including architectural, mechanical and many other elements have to be considered. At the urban scale, the impact on the socio-economic fabric of the reused building’s neighborhood and the infrastructure necessary to support the new building use are challenges to be addressed. The 2nd Colloquium on AI4AEC will explore these and other challenges at the building and urban scales from the perspectives of AEC and AI practitioners and researchers. We are excited to have Deputy Prime Minister Stefan Holy as our keynote speaker in the third sesion of the 2nd AI4AEC Colloquium. Deputy Prime Minister of the Slovak Republic Stefan Holy is the Deputy Prime Minister of the Slovak Republic. Professor of Photogrammetry and Remote Sensing, Civil, Environmental and Geomatic Engineering, ETH Zurich Konrad Schindler received his Ph.D. degree from Graz University of Technology, Austria, in 2003. He has been a tenured professor of Photogrammetry and Remote Sensing with ETH Zurich since 2010. He was Commission President of the International Society for Photogrammetry and Remote Sensing (2012-2016), has served as associate editor or senior program committee member for leading remote sensing journals and computer vision conference, and is a member of the Commission for Remote Sensing at the Swiss Academy of Sciences (scnat), and a founding member of the European Laboratory for Learning and Intelligence Systems (ELLIS) and of the ETH AI Center. His research interests lie at the interface of machine learning, computer vision, remote sensing and Earth observation. His passion is harnessing the power of modern, statistically based machine learning to solve real-world mapping problems. Adjunct Professor, Civil and Environmental Engineering, Stanford University Ben Schwegler is an Adjunct Professor of Engineering at Stanford University, a Senior Research Fellow at Engie, and one of the founders of Synthetic Applied Biology, Inc. For most of his career he was the Chief Scientist at Walt Disney Imagineering where he led much of Disney’s Research and Development group. While he was with Disney he lived for a decade in Shanghai China, as well as in Paris, Tokyo, and Hong Kong. Now he is spending most of his time as a Senior Research Fellow for Engie, formalizing the basis for Integrated Infrastructure Systems at Stanford (and teaching a class about that) and getting his startup off the ground. Professor of Geography and Urbanization Science, School of the Environment, Yale University Karen Seto is the Frederick C. Hixon Professor of Geography and Urbanization Science at the Yale School of the Environment. An urban and land change scientist, she is one of the world's leading experts on contemporary urbanization and global environmental change. She uses satellite remote sensing, field interviews, and modeling methods to understand how urbanization will affect the planet, including land change, food systems, biodiversity, and climate change. She has extensive fieldwork experience in Asia, especially China and India, where she has conducted research for over 20 and 10 years, respectively. Professor Seto has served on numerous national and international scientific bodies. She is a Coordinating Lead Author for the urban mitigation chapter for the IPCC 6th Assessment Report, currently underway, and co-lead the same chapter for the 2014 IPCC 5th Assessment Report. She has served on many U.S. National Research Council (NRC) Committees, including the NRC Committee to the Advise the U.S. Global Change Research Program and the NRC Committee on Pathways to Urban Sustainability. From 2000 to 2008, she was faculty at Stanford, where she held joint appointments in the Woods Institute for the Environment and the School of Earth Sciences. She has received many awards for her scientific contributions, including the Sustainability Science Award from the Ecological Society of America and the Outstanding Contributions to Remote Sensing Research Award from the American Association of Geographers. She is an elected member of the U.S. National Academy of Sciences, the Connecticut Academy of Science and Engineering, and the American Association for the Advancement of Science (AAAS). (All times are in PT timezone) 07.00 - 07.10 AM : Introduction Martin Fischer, Professor, CEE, Stanford University Iro Armeni, Postdoctoral Fellow, ETH Zurich 07.10 - 07.55 AM : Keynote Talk, Legislation enabling the AI use in digital built environment Stefan Holy, Deputy Prime Minister of the Slovak Republic 07.55 - 08.55 AM : Session Presentations 07.55 - 08.15 AM : Design and Operation of Multi-Use Infrastructure Ben Schwegler, Adjunct Professor, Civil and Environmental Engineering, Stanford University 08.15 - 08.35 AM : Global Urbanization Trends and the Imperative for Reuse Karen Seto, Professor, School of the Environment, Yale University 08.35 - 08.55 AM : Change detection and forecasting - an Earth observation perspective Konrad Schindler, Professor, Civil, Environmental and Geomatic Engineering, ETH Zurich 08.55 - 09.35 AM : Panel Discussion w/ Session speakers 09.35 - 09.45 AM : Reflections Martin Fischer, Professor, CEE, Stanford University Iro Armeni, Postdoctoral Fellow, ETH Zurich Postdoctoral Fellow, CEE and CS, ETH Zurich Iro Armeni is a PostDoctoral Researcher at ETH Zurich, conducting interdisciplinary research between Civil Engineering and Machine Vision. Her area of focus is on automated semantic and operational understanding of buildings throughout their life cycle using visual data. She completed her PhD at Stanford University on August 2020, Civil and Environmental Engineering Department, with a PhD minor at the Computer Science Department. Prior to enrolling in the PhD program, Iro received an MSc in Computer Science (Ionian University-2013), an MEng in Architecture and Digital Design (University of Tokyo-2011), and a Diploma in Architectural Engineering (National Technical University of Athens-2009). She is the recipient of the ETHZ Postdoctoral Fellowship, the Google PhD Fellowship on Machine Perception, and the Japanese Government (MEXT) scholarship. Iro has worked as an architect and consultant for both the private and public sector. Professor, CEE, Stanford Unversity Martin Fischer is the Kumagai Professor of Engineering at Stanford University and directs the Center for Integrated Facility Engineering. He is known globally for his work and leadership in developing and applying VDC (Virtual Design and Construction) to increase the productivity of construction project teams, enhance building performance, and create new strategic opportunities for firms in the construction industry. VDC combines a focus on project value with rethinking of workflows supported by digital tools and the timely and productive engagement of the expertise needed to achieve the desired project value. His award-winning research results have been used operationally and strategically by many industrial and government organizations around the world. He has co-authored the book “Integrating Project Delivery” published by Wiley in 2017, written over 100 refereed journal articles and book chapters, and given over 100 keynote lectures on his research. His work has been recognized by the ASCE Peurifoy Construction Research Award and with elections to the National Academy of Construction in the US and the Royal Academy of Engineering Sciences in Sweden.	common_crawl_stanford.edu_364
About Stanford AI Club The Stanford AI Club (SAIC) is the premier AI club at Stanford. We are dedicated to fostering an inclusive and vibrant community for students interested in artificial intelligence. We aim to provide all students from established AI researchers to folks wanting to get into the field with the knowledge and opportunity to engage with AI research & development. Our vision is to serve as a centralized hub for the AI community on campus, connecting everyone from domain experts and practitioners to theorists. We offer a wide range of activities such as student-led research projects, reading groups, workshops, and classes designed to equip members with essential AI skills. We also engages with non-profit organizations with the shared vision of democratizing AI and reducing global inequalities. By hosting small-group discussions and lectures with leading experts like Sam Altman, the club provides invaluable networking and learning opportunities for students passionate about AI.	common_crawl_stanford.edu_365
The Impact of Open-Source AI Models in Anesthesiology The field of Anesthesiology is on the cusp of a significant transformation with byadminFebruary 21, 2024 Bridging the Generation Gap in Anesthesiology Larry Chu, MD Nov 2, 2023 We’ve all heard the stereotypes byadminNovember 2, 2023 Energized Educators Electrified by ChatGPT Takeaways from the AIM Lab’s SEA Workshop November 2, 2023 By Larry Chu, byadminNovember 2, 2023 Where’s the Patient Voice in Anesthesia Residency Education? Opportunities for Growth and Learning April 15, 2023 We recently presented an byadminApril 15, 2023 Banner ConferencesFebruary 21, 2024 The Impact of Open-Source AI Models in Anesthesiology The field of Anesthesiology is on the cusp of a significant transformation with byadmin Conferences FeaturedNovember 2, 2023 Bridging the Generation Gap in Anesthesiology Larry Chu, MD Nov 2, 2023 We’ve all heard the stereotypes byadmin Banner Featured WorkshopsNovember 2, 2023 Energized Educators Electrified by ChatGPT Takeaways from the AIM Lab’s SEA Workshop November 2, 2023 By Larry Chu, byadmin WorkshopsApril 15, 2023 Where’s the Patient Voice in Anesthesia Residency Education? Opportunities for Growth and Learning April 15, 2023 We recently presented an byadmin Featured Stories of SuccessSeptember 1, 2016 Ready from day one Charles Hill, MD, Medical Director of the Stanford Cardiovascular Intensive Care byadmin Stories of SuccessAugust 26, 2016 Lectures, refreshed! Clinical Assistant Professor Maeve Hennessy, MD, came to the AIM Lab with a byadmin Banner Featured Stories of SuccessAugust 18, 2016 How to launch a new venture with confidence Clinical instructor Vivianne Tawfik, MD, PhD, continually seeks new ways to byadmin Grand Rounds TalksMarch 3, 2016 Innovating the Future of Anesthesia March 3, 2016 In a captivating session at Johns Hopkins Anesthesiology Grand byadmin NewsMay 11, 2015 Learnly Secures “Best of Category” Award at IARS We are elated to announce that Learnly has been honored with the “Best of byadmin Grand Rounds TalksMay 10, 2015 Rethinking Anesthesia Education May 11, 2015 Introduction In an engaging talk at the University of Iowa’s byadmin	common_crawl_stanford.edu_366
The Impact of Open-Source AI Models in Anesthesiology The field of Anesthesiology is on the cusp of a significant transformation with byadminFebruary 21, 2024 Bridging the Generation Gap in Anesthesiology Larry Chu, MD Nov 2, 2023 We’ve all heard the stereotypes byadminNovember 2, 2023 Energized Educators Electrified by ChatGPT Takeaways from the AIM Lab’s SEA Workshop November 2, 2023 By Larry Chu, byadminNovember 2, 2023 Where’s the Patient Voice in Anesthesia Residency Education? Opportunities for Growth and Learning April 15, 2023 We recently presented an byadminApril 15, 2023 Educational InformaticsDecember 14, 2012 Stanford 25 App Stanford AIM Lab launches patient exam iPad app This week, the Stanford byadmin Educational InformaticsSeptember 28, 2010 StanMed App Stanford Anesthesia lab launches new iPad app On Tuesday the Stanford Anesthesia byadmin Educational InformaticsJuly 19, 2010 START Online Program New online learning approach prepares doctors for Stanford residencies by Tracie byadmin	common_crawl_stanford.edu_367
The Impact of Open-Source AI Models in Anesthesiology The field of Anesthesiology is on the cusp of a significant transformation with byadminFebruary 21, 2024 Bridging the Generation Gap in Anesthesiology Larry Chu, MD Nov 2, 2023 We’ve all heard the stereotypes byadminNovember 2, 2023 Energized Educators Electrified by ChatGPT Takeaways from the AIM Lab’s SEA Workshop November 2, 2023 By Larry Chu, byadminNovember 2, 2023 Where’s the Patient Voice in Anesthesia Residency Education? Opportunities for Growth and Learning April 15, 2023 We recently presented an byadminApril 15, 2023 Conferences FeaturedNovember 2, 2023 Bridging the Generation Gap in Anesthesiology Larry Chu, MD Nov 2, 2023 We’ve all heard the stereotypes byadmin Banner Featured WorkshopsNovember 2, 2023 Energized Educators Electrified by ChatGPT Takeaways from the AIM Lab’s SEA Workshop November 2, 2023 By Larry Chu, byadmin Featured Stories of SuccessSeptember 1, 2016 Ready from day one Charles Hill, MD, Medical Director of the Stanford Cardiovascular Intensive Care byadmin Banner Featured Stories of SuccessAugust 18, 2016 How to launch a new venture with confidence Clinical instructor Vivianne Tawfik, MD, PhD, continually seeks new ways to byadmin	common_crawl_stanford.edu_368
The Impact of Open-Source AI Models in Anesthesiology The field of Anesthesiology is on the cusp of a significant transformation with byadminFebruary 21, 2024 Bridging the Generation Gap in Anesthesiology Larry Chu, MD Nov 2, 2023 We’ve all heard the stereotypes byadminNovember 2, 2023 Energized Educators Electrified by ChatGPT Takeaways from the AIM Lab’s SEA Workshop November 2, 2023 By Larry Chu, byadminNovember 2, 2023 Where’s the Patient Voice in Anesthesia Residency Education? Opportunities for Growth and Learning April 15, 2023 We recently presented an byadminApril 15, 2023 Featured Stories of SuccessSeptember 1, 2016 Ready from day one Charles Hill, MD, Medical Director of the Stanford Cardiovascular Intensive Care byadmin Stories of SuccessAugust 26, 2016 Lectures, refreshed! Clinical Assistant Professor Maeve Hennessy, MD, came to the AIM Lab with a byadmin Banner Featured Stories of SuccessAugust 18, 2016 How to launch a new venture with confidence Clinical instructor Vivianne Tawfik, MD, PhD, continually seeks new ways to byadmin	common_crawl_stanford.edu_369
Takeaways from the AIM Lab’s SEA Workshop November 2, 2023 By Larry Chu, MD An Electric Atmosphere The chatty hubbub filling the room at the Society for Education in Anesthesiology’s 2023 Annual Meeting was not just idle chatter – it was anesthesia educators buzzing with excitement about the potential of ChatGPT to transform teaching and learning. The jam-packed interactive workshop led by Stanford’s Anesthesia Informatics and Media Lab on November 2nd had participants laser-focused on this hot new AI tool, eager to harness its power to bridge artificial and human intelligence for the future of anesthesiology education. ChatGPT is like a teenaged love – infatuating and full of potential, but requiring thoughtful guidance. Janak Chandrasoma, MD From insightful talks unpacking ChatGPT’s inner workings to hands-on small groups strategizing real-world applications, attendees dove headfirst into exploring how to integrate this emerging technology into innovative workflows at their home institutions. The energetic atmosphere was electric with the promise of equipping anesthesiology educators nationwide with the digital literacy and critical perspectives to guide the next generation. Unpacking ChatGPT’s Potential The session kicked off with an eye-opening word cloud activity led by OHSU medical student Tomoko Wilson, which revealed participants’ interest in understanding ChatGPT’s capabilities and limitations specifically for anesthesia education. Dr. Janak Chandrasoma then motivated the workshop with an insightful talk, comparing ChatGPT to past technologies like mixed tapes. His analogies underscored that while new technologies make processes easier, we shouldn’t cling to old difficult ways out of nostalgia. As Dr. Chandrasoma noted, “Rather, we should thoughtfully integrate tools like ChatGPT to enhance learning.” 2023-24 Stanford AIM Lab Informatics fellow, Alex Goodell, MD, gave a foundations overview explaining how ChatGPT works through machine learning, priming participants for hands-on exploration. Strategizing Real-World Applications The highlight of the day was our small group breakout sessions focused on real-world use cases, including creating patient education materials, supporting self-paced learning, and accelerating research. Led by our diverse team of physicians, learners, and educators, these stimulating discussions enabled participants to experience ChatGPT’s potential firsthand. Perspectives Across Generations Our diverse workshop faculty featured insightful perspectives from educators at all career stages, including OHSU medical student Tomoko Wilson and patient education specialist Melissa Hicks. Tomoko masterfully facilitated the opening word cloud activity that mapped attendees’ interests and she facilitated a breakout session on the use of ChatGPT for self-paced learning, demonstrating her impressive leadership as an emerging physician-educator. Her contributions underscored the importance of learner perspectives in innovating education. As a medical student, I’m excited yet mindful about AI. This workshop modeled how we can thoughtfully harness emerging tech to empower tomorrow’s learners. Tomoko Wilson, MD Candidate OHSU Meanwhile, Melissa lent invaluable insights from the patient education frontier. Her guidance in the hands-on breakout focused on creating patient-centered materials highlighted how tools like ChatGPT can make health education more accessible and effective. Tomoko and Melissa’s impactful involvement emphasized the power of bringing broad viewpoints together to evaluate and integrate exciting new tools like ChatGPT. Dr. Viji Kurup wrapped up with an important debrief that included the ethical use of AI like ChatGPT in medical education. Dr. Kurup emphasized that “While ChatGPT presents exciting new opportunities, we must critically evaluate how to ethically integrate it into our teaching, including responsible non-disclosure of PHI on these platforms.” Dr. Larry Chu, our AIM Lab Director, masterfully emceed the workshop, keeping the energy high. In his closing remarks, Dr. Chu noted, “The enthusiastic reception here shows anesthesiology educators’ eagerness to build digital literacy and harness new tools like ChatGPT to prepare future generations.” Key Takeaways Participants left equipped not only with practical skills for integrating ChatGPT at their institutions, but also a framework for critically evaluating AI to enhance anesthesiology education while navigating its limitations. The buzzing excitement filling the room exemplified anesthesiology educators’ appetite to explore emerging technologies like ChatGPT for innovating teaching and learning. Attendees clearly recognized the promise of this new AI tool, while thoughtfully discussing its responsible integration. The successful reception at this SEA workshop highlighted anesthesiology educators’ commitment to guiding the digital transformation of education. We look forward to seeing how attendees creatively integrate what they’ve learned to bridge artificial and human intelligence, opening new dimensions for preparing learners. The future of anesthesiology teaching and learning is undeniably bright!	common_crawl_stanford.edu_370
July 24, 2013 Larry Chu, MD I recently visited Northwestern and presented a workshop on Hacking EdTech. Here are a few items we discussed and links to relevant resources: - Hacking Google Docs for EdTech - Hacking your iPhone - Phocus 3 Lens bundle for iPhone 5 - Camera Pistol Grip - Audiotechnica Lavelier Microphone - iPhone 3.5mm Adaptor Cable (You will need this to plug any microphone into the iPhone) - Younguo LED ring light (add a paper diffuser and you’ll have a good supplementary light source for your next shoot) - Favorite Video Hacking Resources - Cheesy Cam (Aim for professional results with low cost resources) - Abelcine (My “go to” source for professional video equipment, but they also have online tutorials and videos that are great) - TheC47 (Jem Shofield is a master instructor on instructional video production!) - The Location Crew (Dean Miles knows everything about professional sound production) - What we’re using in the AIM Lab - Panasonic HMC150 (our run-and-gun “go to” camera) - Canon C100 (our cinema style camera, we have two for instructional videos and interviews) - Sennheiser EW100 G3 Wireless Lavelier Microphones (make sure to get the correct frequency range for your geographical shooting location) - Dedo Lights (expensive, but lightweight and portable location lighting) - GoPro Black Edition (we’ve tried all sorts of POV cameras and these are our favorite. The black edition shoots 4K and 720p 120 fps, and has the remote and wifi built-in) - Expensive (but best performing) lavelier microphone, on our wishlist. - Inexpensive wired lavelier microphone for less than $20. - Apps and Websites - Present.me is the easiest instructor-centric method of recording narrated powerpoint presentations and sharing them through the cloud. - LearnDash is a reasonable WordPress-based MOOC LMS. The developer Justin Ferriman is dedicated to improving the platform and regularly releases updates. - Tin Can API is a promising method to log LMS interactions (and much more!). It is an evolving project and we are excited to see where it might take things. - Moodle is the quintessential educator-centric LMS and what we use to deploy many of our projects. It is powerful and free, but has a bit of a learning curve. Customizing the UI requires special expertise.	common_crawl_stanford.edu_371
Clinical instructor Vivianne Tawfik, MD, PhD, continually seeks new ways to engage and support the next generation of physician-scientists. When she joined the newly-minted Society of Early-stage Anesthesiology Scholars, she knew the group needed a strong web presence, and she had great ideas about creating an online “academic home” for early-career anesthesia scholars. As a complete novice in web design and development, however, she found herself frustrated as she attempted to realize that vision. She contacted the AIM Lab for technical assistance in getting the site up and running. In Dr. Tawfik’s own words: “The AIM Lab not only built the framework for our website based on the specifications I gave them, but they also took the time to meet with me for a tutorial on using WordPress. I am now proud to say that I can edit the website myself, and have made major additions of content and form to the site!” Do you have questions about how to improve the design and functionality of your website? Need a quick tutorial on how to make WordPress a friend instead of a foe? The AIM Lab is proud to serve the Department of Anesthesiology. Contact us to learn more about how we can help you accomplish your goals! The AIM Lab not only built the framework for our website based on the specifications I gave them, but they also took the time to meet with me for a tutorial on using WordPress. I am now proud to say that I can edit the website myself, and have made major additions of content and form to the site! Vivian Tawfik, MD, Associate Professor of Anesthesiology	common_crawl_stanford.edu_372
Engage and Empower Me: Myths and Truths of Designing for Patient Behavior A NEW COURSE ON PATIENT ENGAGEMENT DESIGN. WINTER QUARTER 2014. ANES 205 Course description: What happens when the $3 trillion U.S. healthcare industry suddenly hinges on patient engagement? Hear the unforgettable patient stories and real-life experiences of successful patient engagement from the industry leaders in healthcare, wellness, and behavior sciences. Understand the neuroscience behind why doctors can’t simply scare patients and employers can’t simply bribe employees into healthy behaviors. Discover how including patients in the design process can empower them to work with their healthcare team to improve care. Learn where health educators, designers, techies, and investors got it wrong and what they can do to improve their success. This colloquium offers rapid-fire panels of guest speakers, real patients and Stanford faculty. Course Directors: Larry Chu, MD, and Kyra Bobinet, MD ANES 204: Medical Education in the New Millennium CHALLENGES AND OPPORTUNITIES FOR INNOVATION AND DIGITAL DISRUPTION Course description: Today’s Millennial medical learners are often disappointed by legacy instructional design that fails to address their unique learning preferences. The volume of new medical knowledge is outpacing our ability to organize and retain it. How might educators disrupt outdated practices through thoughtful use of technology and learning design? How might MOOCs, social media, simulation and virtual reality change the face of medical education? How might we make learning continuous, engaging, and scalable in the age of increasing clinical demands and limited work hours? This interdisciplinary course features thought leaders and innovators from medical education, instructional design, and learning technologies. Course Directors: Larry Chu, MD, Kyle Harrison, MD and Nikita Joshi, MD ANES 206: Design for Health APPLY DESIGN THINKING TO IMPROVE HEALTHCARE. SPRING QUARTER 2014. ANES 206 Course description: This colloquium course will broadly explore the crucial role design can play in optimizing healthcare. We will explore principles of design-thinking and patient-centered design, and examine their impact on innovating solutions to healthcare problems. We’ll hear patients speak about their challenges and learn about their real-world solutions and insights into the nature of health and illness. From designing hospitals and communities to shape individual behavior, to learning about designing for service innovations, mobile apps or emerging health technologies, we’ll have design experts share their knowledge and design stories in order to understand how healthcare providers might better design for health. Course Directors: Larry Chu, MD, Kyle Harrison, MD and Nikita Joshi, MD	common_crawl_stanford.edu_373
Stanford AIM Lab launches patient exam iPad app This week, the Stanford Anesthesia Informatics and Media Lab (AIM) released a new iPad application as part of the Stanford Medicine 25 initiative, a series of hands-on workshops teaching 25 essential techniques for examining patients. Abraham Verghese, MD, who designed the Stanford Medicine 25, invited the AIM Lab to collaborate on the medical app for the initiative. The app was developed by AIM Lab director Larry Chu, MD, and Kyle Harrison, MD, clinical assistant professor of anesthesia. Chu and Harrison note in the app description that the educational tool is meant to be used as a reference tool rather than a replacement for the Stanford Medicine 25 curriculum: The Stanford 25 consists of hands-on sessions in small groups—you can’t substitute for that, and we don’t try to. This app simply provides a place where our students and residents can go to remind themselves of what they learned, or are about to learn in a hands-on session. The app is available for free in the iTunes store. Read more about how the Stanford Medicine 25 curriculum was created and why the hands-on diagnostic skills are imperative in examining patients in this past Stanford Medicine article. Previously: Stanford medical residents launch iPhone app to help physicians keep current on research, School of Medicine alumni association partners with Doximity to test first-of-its-kind smartphone app, Stanford-developed iPARS app available for download and Stanford anesthesia lab launches educational iPad app	common_crawl_stanford.edu_374
Positions: 1-2 annually Duration: 12 months Website: http://aim.stanford.edu/ Description: This is an innovative new program. Informatics is a broad field encompassing artificial intelligence, cognitive science, computer science, information science, and social science. The goal of this fellowship is to train physicians in informatics so they can be leaders in this growing field. One application for example is the use of Web 2.0 information technologies to improve education, research, and patient care. No experience in computer programming is required for this fellowship. Curriculum: The Fellow will be based in the Stanford Anesthesia Informatics and Media Lab under the direction of Dr. Larry Chu. The lab’s objectves are to: - Innovate new uses of internet- and media-based technologies for research, clinical care and medical education; - Develop and test new informatics approaches to support the broad missions of the Department of Anesthesia at Stanford. Develop new researchers to innovate the field of anesthesia informatics; - Conduct and publish research to generate new knowlege in the area of anesthesia informatics and produce a new generation of anesthesia informatics researchers; - Promote use of informatics and media technology in medicine and teach other clinicians and researchers how to implement these techniques in their own work. Knowledgebase for the Fellowship: Medical Informatics - How web 2.0 and semantic web technologies can be used to drive connectedness to information and how social networks can be utilized to improve anesthesia education, clinical care and patient safety Informatics Tools - How to design effective human-computer interfaces for informatics applications in medicine and anesthesia Multimedia and Informatics Production Workflow - How to create basic informatics applications using best practices and principals of good design How to produce and manage these projects - How to publish and promote medical informatics or multimedia projects Continuous Quality Improvement - Learn principles of continuous quality improvement - Apply principles to anesthesia informatics projects Crisis Management and Patient Safety - Learn principles of crisis management and applications to patient safety initiatives - Apply these principles to anesthesia informatics projects - Develop new simulation and patient safety initiatives using virtual networked - environments Statistics and Data Analysis - Understand how to conduct basic quantitative and qualitative analyses of research data in - order to interpret results and draw conclusions from informatics studies Measurement Science - How to design, test and deploy surveys to evaluate informatics applications Research Project Fellows will learn how to design and conduct research studies to evaluate the efficacy of informatics applications in the clinical and educational environments. Fellows will begin by selecting a research project with advice from faculty. The project can focus on clinical or educational informatics projects related to anesthesia. The fellow will be responsible for designing a research study proposal, conducting the study, and analyzing the results and preparing a manuscript for publication. The fellow will audit courses in the University to provide didactic sessions regarding critical principles of informatics. Selection of coursework will be individually tailored for each fellow through discussion with the Fellowship director based on previous experience and educational needs, but may include: Stanford EDUC 151: Introduction to Qualitative Research Methods Stanford CS 106A: Programming Methodology (ENGR 70A) Stanford CS 106A: Programming Methodology (ENGR 70A) Stanford CS 147: Introduction to Human-Computer Interaction Design Stanford EDUC 147X: Human-Computer Interaction in Education Stanford EDUC 151: Introduction to Qualitative Research Methods Stanford HRP 223: Epidemiologic Analysis: Data Management and Statistical Programming Monthly seminars and/or journal club provide instruction and opportunity to explore concepts with experts in informatics. Fellows are given reading prior to seminars which are then discussed. The Fellow chooses two projects to work on to gain real life experience in applying principles of informatics. Stanford Anesthesia Informatics fellows in years 2010-11 and 2011-12 will have the unique opportunity to assist in planning and organizing the Fourth World Congress on Social Media and Web 2.0 in Health, Medicine and Biomedical Research, a leading international medical informatics conference which is being organized by Dr. Chu at Stanford University for September 2011. Faculty: Larry Chu, MD, MS (Fellowship Director, Anesthesia Informatics) Alex Macario, MD, MBA (OR Management, Clinical Informatics) Kyle Harrison, MD (Patient Safety, Simulation, Educational Informatics) Bassam Kadry, MD (Clinical Informatics) Salary: Salary for next academic year is $63,726 (based on PGY V level). To pay this salary, fellows work in the Stanford operating rooms. This requirement is fulfilled by working as an attending for 40 days, and take 32 calls. Of the 32 calls, 16 are “first” call sessions, 8 during weeknights (5pm – 7 am) and 8 taken on weekend or holiday days (8 am – 8 am). The remaining 16 calls will be second calls, taken only on weeknights (5pm – 7am). Vacation: 5 weeks not including meeting time. Application: The application requires a Curriculum Vitae, three letters of recommendation (one from your residency director), Medical School Transcript and Dean’s Letter (you may request your anesthesia program director/coordinator to send this for you), and a one-page essay describing your career plans. Please keep in mind that to take the position at Stanford, you would need a California medical license. You may email information to AIMLABSTANFORD@gmail.com and have physical transcripts and letters of recommendation mailed to: - Stanford AIM Lab, 300 Pasteur Drive, Grant Building Room S268C, Stanford, CA 94305. Phone: 650-723-6632. Fellowship Director and Contact person: Dr. Larry Chu, MD, MS	common_crawl_stanford.edu_375
FAQs Is AI+HEALTH accredited? In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Are CME credits available? Stanford Medicine designates this Live activity for a maximum of 12.00 AMA PRA Category 1 CreditsTM. Physicians should claim only the credit commensurate with the extent of their participation in the activity. For more accreditation details, kindly refer to the Stanford CME website. Is there a student or trainee discount? In order to qualify for the reduced rate for Students, Clinical/Research Trainees or Post-Docs, please complete the registration process and submit this Verification Form including documentation proving your trainees status before 11:59 pm (Pacific Time) on December 3, 2024. Upon successful verification of your registration and documentation, you will receive an email containing an access link to the conference. Are scholarships available? We recognize the financial constraints on organizations locally as well as across the globe and are offering complimentary registration fee for a limited number of attendees. This opportunity is intended to help individuals who could not attend without the reduced rate. To qualify for complimentary registration, attendees must meet both criteria bulleted below: - Practitioners, researchers, executives, policymakers, and professionals working at the intersections of AI + Health. This scholarship program is not intended for students, or for people who have never worked in artificial intelligence or healthcare. - Part of an organization that cannot otherwise afford to participate in AI+HEALTH 2024 The conference team will be accepting scholarship applications on a rolling basis until November 22, 2024. Applicants will receive notification on the scholarship decision within 1-2 weeks. If you have any questions, please contact ai_healthconference@stanford.edu. Can Stanford attendees using STAP funds to cover the registration fee? Yes, to register on STARS for this online event, click here. I will not be able to attend most of the conference in real-time due to other commitments. If you register, will you also have access to all of the recorded talks? Yes, conference registrants will receive access to the recordings for up to 3 months once they are released. Recordings will be published on the conference portal given all panelists/speakers have permitted us to release their session recordings. All conference registrants will be informed once the recordings are available for viewing.	common_crawl_stanford.edu_376
Accepted Posters & AI Demos Poster Presentations Breaking the Barriers to Effective Type 2 Diabetes Care: An NLP-Enabled Decision Support System Solution Valentina Estupiñán-Vargas, Andrés Montoya-López A decision support system for type 2 diabetes care, using NLP on unstructured data and clinical expertise. Provides personalized recommendations based on real-time analysis of patient data from health records. Aims to enhance treatment quality and efficiency, reduce costs, and bridge the gap in specialized healthcare personnel. Characterizing tissue composition through combined analysis of morphologies and transcriptional states Feng Bao, Steven Altschuler, Lani Wu Advances in spatial transcriptomics technologies enable simultaneous profiling of morphological and transcriptional modalities from the same cells or regions within tissues. We present multi-modal structured embedding, an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional measurements. Diagnostic Accuracy of Synthetic FDG PET Images from MR Imaging Helena Zhang, Jiahong Ouyang, Jarrett Rosenberg, Greg Zaharchuk Using a deep learning model, FDG PET images have been synthesized from from multi-contrast MRI. This study aims to evaluate the diagnostic accuracy of synthetically generated FDG PET images compared to actual FDG PET images, in predicting cancer recurrence, using the biopsy-proven and clinically proven diagnoses from MRI images as the reference standard. Do Good with Data: Unlocking the potential of AI for Nonprofits Do Good with Data, Inc Data Science and Artificial intelligence (AI) can be an effective and powerful tool for non-profit organizations. With latest AI technology can be used by nonprofits to effectively increase their outreach and further achieve their goal than what was believed before. Physics Informed Deep Residual Networks Based Type-A Aortic Dissection Prediction Joy Cao, Dr. Min Zhou Acute Type-A aortic dissection is a life-threatening condition. The proposed novel physics-informed deep residual network shows great potential in creating a cost-effective non-invasive predictor framework. We believe that by deploying this predictor, doctors can take appropriate early actions and greatly reduce the mortality of Type-A aortic dissection patients. Predicting FDG-PET Images from Multi-contrast MRI using Deep Learning in Patients with Brain Neoplasms Jiahong Ouyang, Kevin T. Chen, Rui Duarte Armindo, Guido Alejandro Davidzon, Elizabeth Hawk, Farshad Moradi, Jarrett Rosenberg, Ella Lan, Helena Zhang, Greg Zaharchuk FDG PET is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost. On the other hand, MR is widely available and non-invasive. Thus, in this project we aim to generate diagnostic-quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi-contrast MRI. Predicting Novel Drug-Drug Interaction Risks with a Machine-Learning Framework: a Case Study in SARS-CoV-2 Antivirals Dean Wang, Danielle Wang To improve safety and efficacy in SARS-CoV-2 treatment, we developed a pharmacologic profile-based ML algorithm to predict undiscovered drug-drug interactions for three antivirals. All drug combinations were categorized by severity and validated with FAERS. With high predictive accuracy, this model may streamline prescriptions and accelerate novel drug development. Puncture point identification in PCNL surgery using epipolar geometry Pinak Paliwal, Palani Narayan There exists a problem of determining the 3D orientation of puncture needle to renal collecting system in Percutaneous nephrolithotomy (PCNL) surgery. This can be modeled through epipolar geometry to pick the correct orientation. It is possible to determine a good orientation based on camera geometry and training data based on human doctors’ choices. Simple Hardware-Efficient Long Convolutions for Sequence Modeling Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen, Armin W. Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Ré What is the simplest architecture you can use to get good performance on sequence modeling? We study whether directly learning long convolutions over the sequence can match the performance of State Space Models (SSMs) and Transformers, while maintaining a sub-quadratic compute scaling in the sequence length. AI Demos Almanac: A Knowledge Grounded Large Language Model for Physicians William Hiesinger, Cyril Zakka, Akash Chaurasia, Rohan Shad, Alex Dalal, Jennifer Kim, Michael Moor, Kevin Alexander, Euan Ashley, Jack Boyd, Kathleen Boyd, Karen Hirsch, Curtis Langlotz, Joanna Nelson Despite many promising applications in clinical medicine, adoption of Large Language Models (LLMs) in real-world settings has been largely limited by their tendency to generate incorrect and sometimes even toxic statements. In this study, we develop Almanac, an LLM framework augmented with retrieval capabilities for medical guideline and treatment recommendations. BREAST AI: A Low Cost, Explainable Artificial Intelligence Based App for Efficient Diagnosis of Breast Cancer in Developing Areas Vibha Addala Accurate diagnosis of breast cancer is critical to successful treatment; however, those living in developing settings often don’t have access to resources such as mammograms and doctors to get a precise diagnosis. BREAST AI has the potential to diagnose breast cancer in developing settings based on ultrasound scans in a fast, explainable, accessible, and accurate way. Dimble: Orders of Magnitude Faster Medical Imaging IO Chris Ayling, Ben Sand, Tanisha Banaszczyk, Akshay Chaudhari, Rogier van der Sluijs, Arjun Desai, Sarthak Pati, Jake Goulding Dimble is an open source fast DICOM/NIfTI replacement. It is a new format, with easy conversion to/from DICOM. This makes working with medical data for machine learning at scale more efficient and much simpler. DrAid Liver Cancer CT- Detecting early signs of Liver Cancer Steven Truong, VinBrain DrAid- Liver Cancer CT: An AI platform enabling segmentation & early cancer detection of all types of tumors and classifying them into 4 four different classes: HCC, other malignant (other than HCC), benign, and ambiguous., thereby assisting clinicians to speed up medical management. Saccador Isabel Hyo Jung Song, Aarav Sharma, Thuy-Anh Nguyen Saccador effectively leverages computer vision algorithms such as BiseNet and MTCNN to establish a correlation between eye-gaze estimation and saccadic behavior. Saccador offers a novel and unique approach to correlating eye movement to the possibility of abnormal saccades. UMLS Entity Link/Extraction from Unstructured Text (Over 400k concepts) Santosh Gupta, Science.IO Science.IO's premier model, Kepler, can map medical terms from unstructured text to over 400,000 entities in just one API call. Kepler identifies all entity spans within the input text, then links them to established medical ontologies. At present, Kepler offers support for the following medical ontologies: UMLS, ChEMBL/ChEBI, dbSNP, Cell Line Ontology (CLO), GeneID, ClinVar, and NCBI Taxonomy ID	common_crawl_stanford.edu_377
Event Info Overview Agenda Speakers Registration Event Info General Certificate of Attendance / Participation If you are attending the event in person, we can provide you with a certificate of attendance upon request after the event has concluded. If you are attending the event online, we are unable to provide documentation for your participation. Recordings The 2024 AIMI Symposium will be recorded and accessible for free on Stanford AIMI's YouTube channel after the event. CME Credits Continuing medical education (CME) credits are not available for this event. Our AI+HEALTH virtual conference, which will be held on December 10-11, 2024, will provide CME credits. Assistance Please email us at aimicenter@stanford.edu if you need assistance. In-Person Attendees Location In-person attendees will convene at Paul Berg Hall, located on the 2nd floor of the Li Ka Shing Learning and Knowledge Center (291 Campus Drive, Stanford, CA 94305). Parking The closest parking lots are the Stock Farm Lot (L-17) near Welch Rd. at Campus Drive West and Stock Farm Lot (L-18) near Welch Rd. and Oak Rd. Visitor parking for the full day is $36. Please note that all visitor parking payments are contactless and managed through ParkMobile. You can set up an account at parkmobile.io before your visit to save time on the day of the event, or there is also an option to purchase parking through ParkMobile without an account. Please see the Stanford Transportation website for additional details on how to purchase visitor parking. COVID-19 Policy We will be adhering to Stanford University's current COVID-19 Policies. Requirements may change based on county/state regulations. In the event that the in-person AIMI Symposium is canceled due to Covid-19, we will not cover or reimburse any costs related to the canceled event. Online Attendees Location The link to the virtual platform will be emailed to attendees a few days prior to the event. The link will also be posted on the symposium website at that time. Online Q&A We will be using Slido for online attendees to participate in Q&A discussions during the plenary sessions. We encourage you to discuss, upvote, and engage!	common_crawl_stanford.edu_378
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Agenda Main navigation Skip Secondary Navigation Secondary Navigation Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Stanford Health AI Week AI+HEALTH 2024 Main content start Overview Registration Event Info Tuesday, June 3, 2025 8:00am PDT Check-in & Breakfast Available 9:00am Welcome 9:05am Opening Remarks 9:15am Keynote Talk 10:00am Panel 1: Lessons from the Front Lines of Clinical AI 10:45am Break 11:00am Scientific Talks 11:30am Panel 2: Understanding Foundation Models: What Health AI Teams Need to Know 12:15pm Lunch 1:00pm AI Innovation Showcase: Lightning Talks from the Next Generation of Health AI Startups 1:15pm Panel 3: Preparing Clinicians for an AI-Enabled Future 2:00pm Scientific Talks 2:30pm Panel 4: ROI? Why Even Great AI Solutions Struggle to Scale 3:00pm Break 3:15pm Lightning Talks 3:45pm Panel 5: Inside the Editorial Room: Shaping the Future of AI in Medicine 4:15pm Fireside Chat: A Policy Perspective for Moving Clinical AI Forward 4:45pm Closing Remarks 5:00pm Networking Reception Back to Top	common_crawl_stanford.edu_379
Event Info General CME Credits Continuing medical education (CME) credits are not available for this event. Our AI+HEALTH virtual conference, which will be held on December 9-10, 2025, will provide CME credits. Certificate of Attendance / Participation If you are attending the event in person, we can provide you with a certificate of attendance upon request after the event has concluded. If you are attending the event online, we are unable to provide documentation for your participation. Recordings The 2025 AIMI Symposium will be recorded and accessible on Stanford AIMI's YouTube channel after the event. Assistance Please email us at aimicenter@stanford.edu if you need assistance. In-Person Attendees Location In-person attendees will convene at Assembly Hall, located on the 3rd floor of Stanford Hospital (500 Pasteur Drive, Stanford, CA 94305). Parking Self-parking is available at the Pasteur Visitor Garage (200 Pasteur Drive, Palo Alto, CA 94304). Parking rates and additional information can be found here. Online Attendees Location Online attendees will participate in the symposium via the Cvent platform. Instructions will be sent 1 week before the event. We encourage all online attendees to log into the platform before the event to allow time for any technical troubleshooting if needed. Online Q&A We will be using Slido for online attendees to participate in Q&A discussions during the plenary sessions. We encourage you to discuss, upvote, and engage!	common_crawl_stanford.edu_380
Registration Join us to explore what's next for AI in medicine! We’ve introduced a registration fee this year to help support the costs of hosting a large-scale hybrid event. We’re committed to keeping the event as accessible as ever—scholarships are available for those with financial need, and students or trainees can receive discounted rates with verification. We hope you’ll join us! Pediatric Symposium attendees: - If you plan to attend the AIMI Symposium (June 3) and the AIMI Pediatric Symposium (June 4), you can register for both days using the AIMI Symposium link (same as button below). - If you would like to attend the AIMI Pediatric Symposium (June 4) only, please register using the Pediatric Symposium registration link. AIMI Symposium (Day 1) Fees Students & Trainees (including Stanford students & trainees): Please register using the Student & Trainee Registration link. Stanford Faculty/Staff: Please email us at aimicenter@stanford.edu for internal rates. Scholarships AIMI recognizes the financial constraints faced by organizations around the world, including the significant funding uncertainty impacting many academic institutions. To help ensure broad and inclusive participation, we are offering a limited number of scholarships to cover full or partial registration fees for either in-person or virtual attendance. Our goal is to broaden access to the symposium and support individuals who may not otherwise be able to attend due to financial or other barriers. We welcome applicants from around the world and from a range of backgrounds, including students, trainees, early-career professionals, individuals from underrepresented groups, and those working in resource-limited settings. Scholarship Application Deadline: May 31, 2025 Please note that in-person attendance is limited and may reach capacity before the deadline. If in-person participation is no longer available at the time your scholarship application is reviewed, you will automatically be considered for a virtual attendance scholarship.	common_crawl_stanford.edu_381
Student & Trainee Registration Just for students & trainees! We’ve introduced a registration fee this year to help support the costs of hosting a large-scale hybrid event. We’re committed to keeping the event as accessible as ever—scholarships are available for those with financial need, and students or trainees can receive discounted rates with verification. We hope you’ll join us! Student & Trainee Fees Student & Trainee Registration Deadline: May 31, 2025 The student/trainee rate is available to full-time students and clinical or research fellows who follow the registration instructions below. To qualify for this discounted rate, register using the Student & Trainee registration link below and upload documentation verifying your current enrollment or training status. You must register using an institutional email address from your university or academic organization—typically ending in .edu or an international equivalent. Students & trainees using a general email address (e.g., Gmail, Yahoo, Hotmail, etc) will be denied student & trainee registration, even if the verification documentation is acceptable. Acceptable forms of status verification documentation include: - A valid (current) student ID - An official enrollment letter - Verification of current residency or fellowship Once submitted, your registration will be marked as pending while we review your documentation. You will receive an email notification once your registration is approved or declined. Please note that your registration is not confirmed until you receive an approval notice. Additional Information Social Media Sharing We kindly ask students and trainees receiving the discounted rate to help us spread the word by posting about your participation in the AIMI Symposium on social media—once after registering and again during the event. We appreciate your support in sharing your experience with others. Payment and Processing If your selected ticket type includes a fee—such as in-person attendance or a virtual pass purchased after the Early Bird deadline—payment will be required at the time of registration. - Your credit card will be charged, but the charge will remain pending until your registration is confirmed. - If your registration is approved, the payment will be processed upon approval. - If your registration is declined, the charge will be canceled. Please allow up to 7-10 business days for the cancellation to reflect. - If you select a free ticket (e.g., Early Bird virtual pass), no payment will be required at the time of registration. In-Person Attendance Capacity for in-person participation is limited and may be reached before the registration deadline. If in-person passes are no longer available when your documentation is reviewed, you will automatically be considered for a virtual pass.	common_crawl_stanford.edu_382
Healthcare AI Blog Ensuring the Fairness of Algorithms that Predict Patient Disease Risk Decision-support tools for helping physicians follow clinical guidelines are increasingly using artificial intelligence, highlighting the need to remove bias from underlying algorithms. Read hereNew AI-Driven Algorithm Can Detect Autism in Brain “Fingerprints” Led by AIMI faculty Kaustubh Supekar, Stanford scholars have created an algorithm that uses functional MRI scans to find patterns of neural activity in the brain that indicate autism. Read hereBroadening the Use of Quantitative MRI, a New Approach to Diagnostics A promising technology is held back by lack of quality data, but with a newly released dataset, Stanford researchers are about to set it free. Read hereDe-Identifying Medical Patient Data Doesn’t Protect Our Privacy AIMI co-director, Nigam Shah, makes the case that de-identifying health records used for research doesn’t offer anonymity and hinders the learning health system. Read hereWhen Algorithmic Fairness Fixes Fail: The Case for Keeping Humans in the Loop Attempts to fix clinical prediction algorithms to make them fair also make them less accurate. Read here	common_crawl_stanford.edu_383
EchoNet-LVH Download here When citing this dataset in your research or other publications, please reference the following DOI: https://doi.org/10.71718/wtrx-wj56. Proper attribution ensures the continued accessibility and credibility of the dataset for the scientific community. Dataset Description Echocardiography, or cardiac ultrasound, is the most widely used and readily available imaging modality to assess cardiac function and structure. Combining portable instrumentation, rapid image acquisition, high temporal resolution, and without the risks of ionizing radiation, echocardiography is one of the most frequently utilized imaging studies in the United States and serves as the backbone of cardiovascular imaging. For diseases ranging from heart failure to valvular heart diseases, echocardiography is both necessary and sufﬁcient to diagnose many cardiovascular diseases. In addition to our deep learning model, we introduce a new large video dataset of echocardiograms (parasternal long axis view) for computer vision research. The EchoNet-LVH dataset includes 12,000 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac chamber size and wall thickness. Canonical URL https://echonet.github.io/lvh/	common_crawl_stanford.edu_384
INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis Download here When citing this dataset in your research or other publications, please reference the following DOI: https://doi.org/10.71718/aqrb-et67. Proper attribution ensures the continued accessibility and credibility of the dataset for the scientific community. Dataset Description Synthesizing information from various data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimodal medical datasets. To address this limitation, we introduce INSPECT, which contains de-identified longitudinal records from a large cohort of pulmonary embolism (PE) patients, along with ground truth labels for multiple outcomes. INSPECT contains data from 19,438 patients, including CT images, sections of radiology reports, and structured electronic health record (EHR) data (including demographics, diagnoses, procedures, and vitals). Using our provided dataset, we develop and release a benchmark for evaluating several baseline modeling approaches on a variety of important PE related tasks. We evaluate image-only, EHR-only, and fused models. Trained models and the de-identified dataset are made available for non-commercial use under a data use agreement. To the best our knowledge, INSPECT is the largest multimodal dataset for enabling reproducible research on strategies for integrating 3D medical imaging and EHR data. NOTE: this is the first part of release due to PHI review. This release has 20078 CT scans, 21,266 impression sections and the EHR modality data will be uploaded to Stanford Redivis website (https://redivis.com/Stanford)	common_crawl_stanford.edu_385
LERA- Lower Extremity RAdiographs Download here When citing this dataset in your research or other publications, please reference the following DOI: https://doi.org/10.71718/aqrb-et67. Proper attribution ensures the continued accessibility and credibility of the dataset for the scientific community. Dataset Description In this retrospective, HIPAA-compliant, IRB-approved study, we collected data from 182 patients who underwent a radiographic examination at the Stanford University Medical Center between 2003 and 2014. The dataset consists of images of the foot, knee, ankle, or hip associated with each patient. Canonical URL https://aimi.stanford.edu/lera-lower-extremity-radiographs Full Description Musculoskeletal disorders (MSDs), which encompass a wide variety of bone, soft tissue, and joint abnormalities, are a major healthcare challenge around the world. MSDs are typically diagnosed using radiographs; however, variations in diagnostic interpretation quality can often lead to diagnostic errors. This problem is often compounded by a lack of available tools to triage large volumes of unread examinations, which can result in numerous adverse downstream effects related to delay of diagnosis and treatment. The recent revolution in deep learning techniques for image analysis suggests that convolutional neural networks (CNNs) can serve as an effective tool for computer-aided detection of radiograph abnormalities. To aid computational models in accurately identifying diverse abnormalities in highly-variable radiographs of multiple body parts, we are releasing LERA (Lower Extremity RAdiographs). This dataset was used as the held-out test set in our recent study, which found that a single pre-trained CNN was effective in performing generalized abnormality detection in lower extremities [citation after publication]. Dataset Details In this retrospective, HIPAA-compliant, IRB-approved study, we collected data from 182 patients who underwent a radiographic examination at the Stanford University Medical Center between 2003 and 2014. The dataset consists of images of the foot, knee, ankle, or hip associated with each patient. Assignment of Labels Our dataset includes a .csv file matching patient identification numbers to diagnosis labels and radiograph types. Diagnosis labels were assigned as follows. After prospective evaluation of all radiographs associated with a patient, the attending radiologist at the time of initial interpretation assigned each patient a binary classification of normal (y=0) or abnormal (y=1). The designation of a radiograph as normal refers to the attending radiologist’s interpretation of a radiograph as normal given the age of the patient; all radiographs that fall outside this categorization are designated as abnormal (which may be as varied as degeneration, hardware, arthritis, and fractures, among others). Due to these loose constraints as well as the fact that ground truth in radiographic examinations can be difficult to establish, we predicted that the dataset contained a small percentage of incorrect labels; in order to correct for this, two board-certified radiologists, each with 6 years of post-graduate experience, independently labeled the images in this dataset through majority vote consensus between the two radiologists and the prospective exam report. As a result, we are confident that dataset is highly accurate and will serve a suitable resource for testing deep learning models. Additional Information Please note that all labels are assigned at the patient level, indicating that the same classification applies to all images for a particular patient. Also, since images were collected over a twelve-year period, the dataset includes highly variable images, with radiographs in varying in size, resolution, and color; there may also be duplicate images. This study was supported by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). The research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under Award Number R01LM012966 and Stanford Child Health Research Institute (Stanford NIH-NCATS-CTSA Grant #UL1 TR001085). This research used data or services provided by STARR, "STAnford medicine Research data Repository,” a clinical data warehouse made possible by the Stanford School of Medicine Research Office. Terms & Conditions Stanford University School of Medicine LERA- Lower Extremity RAdiographs Dataset Research Use Agreement By registering for downloads from the LERA- Lower Extremity RAdiographs Dataset, you are agreeing to this Research Use Agreement, as well as to the Terms of Use of the Stanford University School of Medicine website as posted and updated periodically at http://www.stanford.edu/site/terms/. 1. Permission is granted to view and use the LERA- Lower Extremity RAdiographs Dataset without charge for personal, non-commercial research purposes only. Any commercial use, sale, or other monetization is prohibited. 2. Other than the rights granted herein, the Stanford University School of Medicine (“School of Medicine”) retains all rights, title, and interest in the LERA- Lower Extremity RAdiographs Dataset. 3. You may make a verbatim copy of the LERA- Lower Extremity RAdiographs Dataset for personal, non-commercial research use as permitted in this Research Use Agreement. If another user within your organization wishes to use the LERA- Lower Extremity RAdiographs Dataset, they must register as an individual user and comply with all the terms of this Research Use Agreement. 4. YOU MAY NOT DISTRIBUTE, PUBLISH, OR REPRODUCE A COPY of any portion or all of the LERA- Lower Extremity RAdiographs Dataset to others without specific prior written permission from the School of Medicine. 5. YOU MAY NOT SHARE THE DOWNLOAD LINK to the LERA- Lower Extremity RAdiographs dataset to others. If another user within your organization wishes to use the LERA- Lower Extremity RAdiographs Dataset, they must register as an individual user and comply with all the terms of this Research Use Agreement. 6. You must not modify, reverse engineer, decompile, or create derivative works from the LERA- Lower Extremity RAdiographs Dataset. You must not remove or alter any copyright or other proprietary notices in the LERA- Lower Extremity RAdiographs Dataset. 7. The LERA- Lower Extremity RAdiographs Dataset has not been reviewed or approved by the Food and Drug Administration, and is for non-clinical, Research Use Only. In no event shall data or images generated through the use of the LERA- Lower Extremity RAdiographs Dataset be used or relied upon in the diagnosis or provision of patient care. 8. THE LERA- Lower Extremity RAdiographs DATASET IS PROVIDED "AS IS," AND STANFORD UNIVERSITY AND ITS COLLABORATORS DO NOT MAKE ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS LERA- Lower Extremity RAdiographs DATASET. 9. You will not make any attempt to re-identify any of the individual data subjects. Re-identification of individuals is strictly prohibited. Any re-identification of any individual data subject shall be immediately reported to the School of Medicine. 10. Any violation of this Research Use Agreement or other impermissible use shall be grounds for immediate termination of use of this LERA- Lower Extremity RAdiographs Dataset. In the event that the School of Medicine determines that the recipient has violated this Research Use Agreement or other impermissible use has been made, the School of Medicine may direct that the undersigned data recipient immediately return all copies of the LERA- Lower Extremity RAdiographs Dataset and retain no copies thereof even if you did not cause the violation or impermissible use. In consideration for your agreement to the terms and conditions contained here, Stanford grants you permission to view and use the LERA- Lower Extremity RAdiographs Dataset for personal, non-commercial research. You may not otherwise copy, reproduce, retransmit, distribute, publish, commercially exploit or otherwise transfer any material. Limitation of Use You may use LERA- Lower Extremity RAdiographs Dataset for legal purposes only. You agree to indemnify and hold Stanford harmless from any claims, losses or damages, including legal fees, arising out of or resulting from your use of the LERA- Lower Extremity RAdiographs Dataset or your violation or role in violation of these Terms. You agree to fully cooperate in Stanford’s defense against any such claims. These Terms shall be governed by and interpreted in accordance with the laws of California.	common_crawl_stanford.edu_386
Executive Education A curated learning experience with Stanford experts—designed for leaders, innovators, and investors. The Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) offers a limited number of customized, private education sessions designed for groups interested in gaining a deeper understanding of how AI is shaping the future of healthcare. These exclusive engagements provide access to world-class faculty and researchers working at the forefront of AI in biomedicine. Whether you're a visiting delegation, family office, corporate leadership team, or strategic investor, these events are tailored to your group's interests—ranging from foundational overviews to in-depth conversations about clinical implementation, commercialization pathways, and cutting-edge research. Why Groups Choose AIMI's Custom Sessions - Access to Thought Leaders: Hear directly from Stanford experts shaping the field of AI in healthcare. - Tailored Content: Each session is designed around your priorities—whether that’s imaging, clinical decision support, data governance, implementation, or policy. - Exclusivity: These are not public lectures. Your group will have a dedicated, interactive experience designed just for you. - Strategic Insights: Explore the intersection of academic innovation and real-world commercialization with leaders deeply engaged in translating research into impact. Sample Session Topics - The Future of AI in Health and Medicine - What It Takes to Implement Clinical AI - Building a Data Strategy for AI Success - Investing in AI That Transforms Health - Policy, Ethics, and Regulation in Health AI - Research highlights from AIMI labs and collaborators Format & Pricing Pricing reflects the high level of faculty involvement, content development, and coordination required. Sessions can be held in-person at Stanford University or online, and include time for discussion and Q&A. Strategic Affiliates of the Stanford AIMI Industry Affiliate Program receive one or more full-day visits with networking each year as part of their membership, along with additional benefits. Contact Info To inquire about a private session or explore AIMI’s Industry Affiliate Program, please contact: Johanna Kim, AIMI Executive Director	common_crawl_stanford.edu_387
Summer Research Internship Summer 2025 Program Application Update Acceptance notifications for the 2025 Stanford AIMI Summer Research Internship have been sent. Please check your inbox (and spam folder) for your decision email. Due to high volume, we are unable to respond to individual inquiries about application status. If you applied to the Summer Health AI Bootcamp, you can expect to hear back during the week of May 19, 2025. Thank you for your patience. Watch the recording of our 2025 Summer Programs Info Session Here For a comprehensive overview of our two summer programs, please see our Info Booklet for Summer 2025 Programs for High School Students! The Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) is committed to advancing AI research in medicine for public good, promoting health equity and excellence in healthcare. In this spirit, we are excited to launch our annual Summer Research Internship for high school students interested in exploring technical and clinical aspects of AI in healthcare. Program Overview During this two-week virtual program, we aim to spark interest and empower the next generation of AI pioneers in medicine. On a day-to-day, the internship generally consists of: - Introductory and technical lectures on AI in healthcare fundamentals - Mentoring activities with Stanford Student Leads and researchers - Hands-on group working sessions for research projects - Social activities - Virtual Career Lunch and Learns with guest speakers representing academia, industry, non-profit, government. etc. An extended independent research internship opportunity may be available for interested program participants. Our hope is to inspire students to develop innovative AI solutions to advance human health. Students who attend the entirety of the internship will receive a Certificate of Completion once the program is complete. This year we will also be holding an AI in Medicine Bootcamp program for high school students. Learn more about the Bootcamp here. We will be adhering to Stanford's policies for COVID-19 and programs and activities involving minors. A recording of the AIMI High School Summer Program (Research Internship & AI Bootcamp) Info and Q&A Session that was held on January 20, 2024, can be viewed here. Key Dates - AIMI Summer High School Programs Info Session (Virtual): December 9th, 2024. View the recording here. - Summer Research Internship Application Opens: December 9th, 2024 - Fee Waiver and Scholarship Application Deadline: February 21st, 2025 - Summer Research Internship Application Deadline: February 28th, 2025 - Summer Research Internship Dates: June 16th-27th, 2025 \| 9am-1pm PT (Online) *IMPORTANT NOTE: Stanford University will be on winter closure from December 23, 2024, to January 3, 2025, and no applications (including Financial Aid) will be reviewed during this period. Eligibility - Must be high school students (entering 9th grade through 12th grade in Fall 2025) - Must be over the age of 14 by the start of the program - Strong preference for students with strong math and/or computer programming skills and/or experience with a healthcare project - Due to limited space, we expect students to commit to participating for the full-day agenda (9am-1pm) throughout the entirety of the 2-week program - Must a U.S. citizen or permanent resident, or provide documentation of valid visa status Stanford AIMI embraces inclusion, integrity, diversity, and team-science as pillars for success. We encourage students from all backgrounds to apply, particularly students from groups under-represented in AI, including, but not limited to, first generation students, students from varying socioeconomic backgrounds, students with disabilities, students who are members of federally recognized tribes, students who have been underrepresented in the field on the basis of gender identity or expression of sexual orientation, or students with work, educational, or life experiences that contribute to the diversity of the field of AI in medicine. Applications are open to individuals of all backgrounds and will be reviewed and selections made in conformance with applicable law. Fees - Application Fee: $40 - Program Participation Fee (if accepted): $850 Application The online application consists of questions related to your background, short-essay questions and uploading your unofficial transcript. You will receive a copy of your completed application upon successful submission. Applications for Summer 2025 are now closed. Application Fee Waiver and Scholarships The AIMI Summer Research Internship and Summer Health AI Bootcamp program offers financial aid opportunities to support eligible students. The deadline to apply for financial aid is Friday, February 21st, 2025 at 11:59pm PT (1 week before the deadline to apply for both programs, which is Friday, February 28th, 2025). To apply, students must complete a separate financial aid form by the February 21st, 2025 deadline. If accepted for financial aid: - The $40 application fee will be waived. - If also accepted into the program, up to $850 of the total participation fee will be waived. If you are applying for financial aid, please wait to submit your internship/bootcamp application until you receive our financial aid decision, as application fees are non-refundable once paid. Eligibility Criteria for Financial Aid: To qualify, you must meet one of the following criteria, along with providing the corresponding proof: - Gross Family Income Below $80,000 per Year - Proof required: A copy of the family’s 2023 tax return showing total annual income. - Enrollment in a Federal, State, or Local Program Supporting Low-Income Families - Proof required: Official documentation on letterhead from the program confirming the student's enrollment. - Demonstration of Financial Need - Proof required: A letter from a high school principal, high school counselor, financial aid officer, or community leader explaining the student’s financial need. The letter must be on official letterhead and include the name, role, email, and phone number of the person providing the recommendation. Alternatively, the applicant may write a short essay explaining their financial need or extenuating circumstances. Please note, we may schedule a follow-up discussion and request additional documentation to verify the details provided. All applications are subject to verification. Please note that applying for financial aid will not impact your admission decision into the program(s). Applications for Summer 2025 are now closed. Contact Please email aimi-hs-programs@stanford.edu for any questions. Frequently Asked Questions (FAQs) What is the reason for introducing fees for the program, when it was previously offered at no cost? As we continue to expand our Summer Programs for high school students, we are committed to delivering a high-quality, impactful learning experience for our participants. In previous years, we did not charge for participation, but due to the growing number of applicants and our commitment to continually enhancing the curriculum, we have introduced a program fee. This fee allows us to ensure that every student receives the resources, mentorship, and support they need to thrive in the program, while also enabling us to accommodate a larger number of students and maintain a high standard of instruction. The fees help support our dedicated staff and faculty, facilitate access to cutting-edge educational materials, and offer the best possible experience for all participants. We understand that fees may present a challenge for some families, which is why we have established a financial aid process to ensure that qualified students can still participate, regardless of their financial situation. How many students will be accepted into the program? We accept around 25 students into the program. This may be subject to change in 2025. Do you accept applications after the deadline? We receive a high volume of applications during the application period and are not accepting any additional applications past the deadline. Am I eligible to apply if I’m not currently living in the U.S. or if I’m not a U.S. citizen or permanent resident? Due to the overwhelming number of applications we receive, the program will be geared towards students residing in the US. Applicants must be U.S. citizens, permanent residents, or individuals on a valid visa to be eligible. Proof of identity and citizenship, permanent residency, or visa status is required. Applicants must also have a U.S. address for program participation, as the program requires for participants to be based in the U.S. during the program. If your visa status or living arrangements change before or during the program, you are required to notify the program staff within one business day. Can students include letters of recommendation in their application? Letters of recommendation are not a requirement to complete the application and therefore may not be considered during the selection process. Why do I get an error message when trying to access the application link? You are likely logged into a Google account from an organization that does not allow you to share files outside of your organization. Please double check this setting and if the problem persists, try submitting from a non-restricted account. I do not have a background in computer science or coding but am interested in the internship. Can I still apply? We have a strong preference towards students with a strong computer science, math, and/or biology background. Students with little to no coding experience are encouraged to explore the Summer AI Bootcamp. What is the difference between the Summer Research Internship and Summer AI Bootcamp? The Summer Research Internship geared towards students with strong technical skills and is more project-focused, as students are tasked with solving a practical problem in the AI in medicine space with little guidance. The Summer AI Bootcamp is geared towards learners of all levels who are interested in learning about the fundamentals of machine learning in healthcare, and will be more lecture- and discussion-focused. Can I apply for both the Summer Research Internship and Summer AI Bootcamp? Yes, you may apply for both. However, if accepted, you may only participate in one program. Are the AP courses listed in the application required for this internship? What if I am enrolled in the course but haven't taken the course/exam yet? Do you accept IB courses? The AP courses listed in the application are not required for the internship. If you have plans to complete the course/exam or have taken/plans to take a related non-AP course (through IB, community college programs, etc.), there is a section of the application titled "Other Academic Experiences" where you may indicate this. What are the expectations for student participation in this virtual program? The AIMI Summer Research Internship and Summer Health AI Bootcamp are fully virtual experiences, and we expect all participants to be comfortable learning in a digital environment and collaborating with team members online. Daily participation with cameras on is required, and students should be ready to engage actively with instructors, peers, and course materials. This is a dynamic, team-based experience, and your involvement is key to getting the most out of the program. What can interns expect in terms of continuous engagement with Stanford researchers and faculty after the internship program concludes (for ex. publishing research findings in academic journals, poster presentations, etc.)? We cannot guarantee continuous engagement after the internship at this time. The internship project is small in scale and would likely not be sufficient to constitute a publication. Will participating in the internship guarantee a Letter of Recommendation for my college applications? Letters of Recommendation for college applications/future programs from Stanford AIMI Faculty/Staff are not guaranteed, and may depend on a variety of factors such as intern participation, performance, engagement, and quality of work. Participating in the Summer Research Internship will not guarantee admission into a Stanford undergraduate program. Are AIMI Summer Programs credit-bearing? Participation in AIMI Summer Programs is not credit-bearing, meaning that completion of either programs will not directly contribute towards fulfilling academic requirements or earning course credits toward graduation. Instead, it serves as an enrichment opportunity aimed at fostering additional skills and experiences outside the traditional academic curriculum. Do participants of the summer programs receive a Certificate of Completion? Yes, participants of both summer programs will receive a Certificate of Completion, given that they attend and participate for the full duration of the program. Is there any advantage to applying earlier, or do applications get reviewed only after the deadline? Applications are reviewed after the deadline, so there is no advantage to applying earlier. All applications will be considered fairly, regardless of submission time. I made an error in my application, but I already submitted it. Can I go back to re-submit? Unfortunately due to the high volume of applications, we are unable to accommodate any changes to applications once they've been submitted. Only one submission is allowed per applicant.	common_crawl_stanford.edu_388
AIMI Grand Rounds Launch: Developing Clinically Useful AI for Radiology - Curtis Langlotz, MD, PhD Event Details: Location This event is open to: The AIMI Grand Rounds, sponsored by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), is a new series launch that is a crucial initiative for disseminating the latest AI advancements in medicine, aiming to drive transformative innovations in healthcare. It provides healthcare professionals and learners with up-to-date, evidence-based, and transformative knowledge necessary for enhancing clinical decision making and healthcare delivery with AI. This series offers interdisciplinary lectures from renowned AI experts across medicine, engineering, and other fields, sharing cutting-edge research, clinical best practices, and other critical considerations related to AI implementation in healthcare. Participants will gain knowledge and tools to apply AI effectively in their practice, fostering innovation and excellence in patient care, and setting new standards in clinical excellence. Recording will be available soon on the AIMI YouTube Channel. Speaker: Curtis Langlotz, MD, PhD: Director, Center for Artificial Intelligence in Medicine & Imaging; Professor of Radiology, Medicine, and Biomedical Data Science, and Senior Associate Vice Provost for Research, Stanford University Attendance is open to the Stanford and AIMI affiliate community.	common_crawl_stanford.edu_389
AIMI Journal Club: Biologically Informed Deep Neural Network for Prostate Cancer Discovery - Haitham Elmarakeby, PhD Event Details: Virtual Event Stanford community & AIMI affiliates only Paper: Elmarakeby, H.A., Hwang, J., Arafeh, R. et al. Biologically informed deep neural network for prostate cancer discovery. Nature (2021). https://doi.org/10.1038/s41586-021-03922-4 About: Haitham Elmarakeby is an instructor at Dana-Farber Cancer Institute and Harvard Medical School. He is also an affiliate researcher at the Broad Institute of MIT and Harvard. His research is focused on using machine learning to better understand cancer progression and therapeutic resistance in cancer patients. Elmarakeby’s current research focuses on building interpretable models to discover novel markers of clinical and biological outcomes in multiple cancer types including prostate, breast, lung, and melanoma cancers. Elmarakeby earned a BSc from the Systems and Computer Department at Al-Azhar University in Cairo, Egypt and his MSc from the Computer Engineering Department at Cairo University. He completed his PhD in Computer Science at Virginia Tech, and got his postdoctoral training at Dana-Farber Cancer institute. Contact Email: aimicenter@stanford.edu More Information:	common_crawl_stanford.edu_390
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Research Meeting Skip Secondary Navigation Secondary Navigation Event Types All Upcoming Events AI Happy Hour High School Education Conference Journal Club Research Meeting Seminar Grand Rounds Main content start Results for: Research Meeting No events at this time. Please check back later. Back to Top	common_crawl_stanford.edu_391
IBIIS-AIMI Seminar: Facilitating Patient and Clinician Value Considerations into AI for Precision Medicine - Mildred Cho, PhD Event Details: Location This event is open to: Abstract: For the development of ethical machine learning (ML) for precision medicine, it is essential to understand how values play into the decision-making process of developers. We conducted five group design exercises with four developer participants each (N=20) who were asked to discuss and record their design considerations in a series of three hypothetical scenarios involving the design of a tool to predict progression to diabetes. In each group, the scenario was first presented as a research project, then as development of a clinical tool for a health care system, and finally as development of a clinical tool for their own health care system. Throughout, developers documented their process considerations using a virtual collaborative whiteboard platform. Our results suggest that developers more often considered client or user perspectives after changing the context of the scenario from research to a tool for a large healthcare setting. Furthermore, developers were more likely to express concerns arising from the patient perspective and societal and ethical issues such as protection of privacy after imagining themselves as patients in the health care system. Qualitative and quantitative data analysis also revealed that developers made reflective/reflexive statements more often in the third round of the design activity (44 times) than in the first (2) or second (6) rounds. These statements included statements on how the activity connected to their real-life work, what they could take away from the exercises and integrate into actual practice, and commentary on being patients within a health care system using AI. These findings suggest that ML developers can be encouraged to link the consequences of their actions to design choices by encouraging “empathy work” that directs them to take perspectives of specific stakeholder groups. This research could inform the creation of educational resources and exercises for developers to better align daily practices with stakeholder values and ethical ML design. Bio: Dr. Mildred Cho is a Professor in the Department of Pediatrics Division of Medical Genetics and in the Department of Medicine at Stanford University. Dr. Cho's major areas of interest are the ethical and social impacts of genetic research and data science and their applications, including AI and machine learning for precision medicine, gene therapy, the human microbiome, and synthetic biology. Attendance is open to the Stanford and AIMI affiliate community. Please contact aimicenter@stanford.edu for the Zoom link if you would like to attend virtually. A recording of the presentation will be posted on the Stanford AIMI YouTube channel shortly after the event.	common_crawl_stanford.edu_392
Event Details: Location This event is open to: Title: Medical Image Segmentation and Synthesis Abstract: Segmentation and synthesis are two fundamental tasks in medical image computing. Segmentation refers to the delineation of the boundaries of a structure of interest in the image, such as an organ, a tumor, or a lesion. Synthesis refers to images created computationally from other data; common examples include cross-modality synthesis and image denoising. This talk will provide an overview of my lab's recent work in these two broad algorithmic directions in the context of a wide range of medical imaging applications. These driving clinical problems include MR imaging of the brain, OCT imaging of the retina, ultrasound imaging of the placenta, and endoscopic imaging of the kidney. I will also illustrate many problem formulations where synthesis can be used to help segmentation, and vice versa. About: Ipek Oguz is an Associate Professor in the Department of Computer Science at Vanderbilt University, with secondary appointments in Electrical and Computer Engineering and Biomedical Engineering. She received her Ph.D. in Computer Science at the University of North Carolina at Chapel Hill. Prior to joining Vanderbilt, she worked in the Penn Image Computing and Science Laboratory (PICSL) and Center for Biomedical Image Computing and Analytics (CBICA) at the University of Pennsylvania as well as in the Iowa Institute for Biomedical Imaging (IIBI) at the University of Iowa. Her research is in the field of medical image computing and specifically in the development of novel methodology for quantitative medical image analysis, with applications to ophthalmic imaging, obstetric imaging, endoscopic imaging and neuroimaging. Her technical interests include image segmentation, image synthesis and deep learning. She has co-authored more than 200 peer-reviewed journal and conference publications. She was a founding member of the Women in MICCAI Committee, and she is an Associate Editor for the Medical Image Analysis and the Machine Learning for Biomedical Imaging journals. She served as a co-chair of IPMI 2017 and MIDL 2023, and she will be the general chair of IPMI 2025. Attendance is open to the Stanford and AIMI affiliate community. Please contact aimicenter@stanford.edu for the Zoom link if you would like to attend virtually.	common_crawl_stanford.edu_393
AIMI Grand Rounds The AIMI Grand Rounds, sponsored by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), is a new series launch held on every fourth Tuesday of the month that is a crucial initiative for disseminating the latest AI advancements in medicine, aiming to drive transformative innovations in healthcare. It provides healthcare professionals and learners with up-to-date, evidence-based, and transformative knowledge necessary for enhancing clinical decision making and healthcare delivery with AI. This series offers interdisciplinary lectures from renowned AI experts across medicine, engineering, and other fields, sharing cutting-edge research, clinical best practices, and other critical considerations related to AI implementation in healthcare. Participants will gain knowledge and tools to apply AI effectively in their practice, fostering innovation and excellence in patient care, and setting new standards in clinical excellence. CME Credit Information Each session is 1.0 credits: AMA PRA Category 1 Credits™ (1.00 hours); Non-Physician Participation Credit (1.00 hours). Credit can only be recorded via text during or up to 24 hours after the session. You must attend the live session to claim credit. Upcoming Grand Rounds Tuesday, April 22, 2025 RSVP for Webinar Sophia Wang, MD, MS Assistant Professor of Ophthalmology Stanford University Title: Challenges and Opportunities for AI in Eye Care Bio: Dr. Wang is an ophthalmologist specializing in glaucoma and a clinician scientist in the Department of Ophthalmology at Stanford. Her research focuses on developing and evaluating artificial intelligence methods to predict ophthalmic outcomes using electronic health records. Dr Wang's work on developing algorithms to predict glaucoma progression and evaluating the fairness and generalizability of EHR models is funded by the NIH, the American Glaucoma Society, and Research to Prevent Blindness. This presentation will discuss using electronic health records (EHR) to develop AI algorithms for eye care. By presenting several examples motivated by the desire to improve glaucoma care, the presentation will illustrate some of the challenges in using EHR for developing ophthalmology algorithms, such as those related to aggregating and standardizing eye-relevant data, and algorithm fairness and generalizability. The presentation will also discuss some promising initiatives to study and overcome these challenges, including ongoing initiatives to incorporate eye data into multicenter registries and data standardization efforts. Tuesday, May 27, 2025 \| 8:00-9:00am PT RSVP for Webinar Killian Pohl, PhD Professor of Psychiatry & Behavioral Sciences and, by Courtesy, Electrical Engineering Stanford University Title: Crafting Machine Learning Models for Neuroscience Discovery Bio: Dr. Pohl is a Professor of Psychiatry and Behavioral Sciences and, by courtesy, of Electrical Engineering, and the Director of the Computational Neuroscience Laboratory (CNSLab) at Stanford University. The focus of his laboratory is to advance computational neuroscience in identifying biomedical phenotypes that enhance personalized medicine toward the diagnosis and prevention of psychiatric disorders from childhood to old age. The CNSLab identifies phenotypes by coupling findings from unbiased, machine learning-based searches across highly dimensional biological, cognitive, neuroimaging, and behavioral data with insightful interpretations by Dr. Pohl’s clinical collaborators. Dr. Pohl is the principal investigator on awards from Stanford’s Institute for Human-Centered Artificial Intelligence and the National Institute of Health (NIH). Before joining Stanford, Dr. Pohl received his Ph.D. in computer science from the Massachusetts Institute of Technology and was faculty at Harvard, IBM Research, the University of Pennsylvania, and SRI International. Machine learning has had limited impact on improving the diagnosis and prevention of psychiatric diseases as their findings often fail to generalize beyond the neuroscience data they were trained on. In this talk, I will review the most critical challenges in using machine learning to advance discovery in neuroscience. For example, the presence of confounding effects often results in data-driven inference identifying spurious and biased associations. I will show that traditional approaches are often unsuitable for minimizing their effect on 3D brain MRI studies and propose alternative strategies, such as augmenting training data via synthetic 3D MRI generated by conditional diffusion models. I will review findings of the proposed deep learning approaches on large publicly available data sets (such as ABCD study, > 10K samples) and smaller in-house studies (< 100 samples). Date: Tuesday, October 28, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Shreya Shah, MD: Clinical Associate Professor of Medicine, Stanford University Title: Evaluating Generative AI Implementations in Healthcare About: Shreya Shah, MD, FACP is a physician leader in healthcare informatics, board certified in clinical informatics and internal medicine. She is a clinician, educator, and researcher, with special interests in artificial intelligence and health IT usability. As a Medical Informatics Director of Primary Care and Population Health for Stanford Medicine, she leads the design, implementation and optimization of health information technology in support of clinicians and patients at Stanford. She is also an Associate Medical Director of the Stanford Healthcare AI Research Team, also known as the "HEA3RT" team, whose vision is to be a global leader in the implementation, evaluation, and teaching of AI in health and health care. Date: Tuesday, September 23, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Lei Xing, PhD: Jacob Haimson & Sarah S Donaldson Professor, Stanford University Title: Deep and Wide Learning for Enhanced Data-Driven Decision-Making About: Dr. Lei Xing is the Jacob Haimson & Sarah S. Donaldson Professor and Director of Medical Physics Division of Departments of Radiation Oncology and Electrical Engineering (by courtesy) at Stanford University. He obtained his PhD from the Johns Hopkins University. His research is focused on AI in medicine, data science, medical imaging, and clinical decision-making. Dr. Xing is an author on more than 450 publications in high impact journals, an inventor/co-inventor on many issued and pending patents. He is a fellow of AAPM, ASTRO, and AIMBE. He is the recipient of the 2019 Google Faculty Research Award, and 2023 Edith Quimby Lifetime Achievement Award of AAPM, which denotes outstanding scientific achievements in medical physics, influence on the professional development of others, and organizational leadership. Date: Tuesday, August 26, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Ivana Maric, PhD: Assistant Professor of Pediatrics, Stanford University Title: AI for Prediction and Profiling of Maternal and Neonatal Pregnancy Outcomes About: Ivana Maric is an Assistant Professor in the Pediatrics Department at the Stanford University. Her research focuses on applying machine learning and AI to improving maternal and neonatal health. Her main focus has been on developing models for early prediction of pregnancy outcomes that could guide development of low-cost, point of care diagnostic tools applicable globally and especially in low-resource settings. In recognition of her work in this area, she was awarded the Rosenkranz Prize by the Freeman Spogli Institute for International Studies and Stanford Health Policy at Stanford University. She is also a co-recipient of the IEEE Communications Society Best Tutorial Paper Award. Date: Tuesday, July 22, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Roxana Daneshjou, MD, PhD: Assistant Professor of Biomedical Data Science and of Dermatology, Stanford University Title: TBA About: Dr. Daneshjou studied Bioengineering at Rice University before matriculating to Stanford School of Medicine where she completed her MD and a PhD in Genetics with Dr. Russ Altman as part of the medical scientist training program. She completed dermatology residency at Stanford as part of the research track and completed a postdoc in Biomedical Data Science with Dr. James Zou. She currently is the assistant director of the Center of Excellence for Precision Heath & Pharmacogenomics, director of informatics for the Stanford Skin Innovation and Interventional Research Group (SIIRG), a founding member of the Translational AI in Dermatology (TRAIND) group, and a faculty affiliate of Human-centered Artificial Intelligence (HAI) and the AI in Medicine and Imaging (AIMI) centers. Date: Tuesday, June 24, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Geoff Sonn, MD: Associate Professor of Urology, Stanford University Title: The Opportunity for AI to Improve Prostate Cancer Detection and Treatment About: Geoffrey Sonn is an Associate Professor of Urology and, by courtesy, of radiology. He specializes in treating patients with prostate and kidney cancer. He has a particular interest in cancer imaging, MRI-Ultrasound fusion targeted prostate biopsy, prostate cancer focal therapy, and robotic surgery for prostate and kidney cancer. He was the Stanford principal investigator of a major clinical trial using MRI-guided focused ultrasound to treat prostate cancer. The goal of this trial was to treat prostate cancer with fewer side effects than surgery or radiation. His research focuses on application of deep learning to improve diagnosis and treatment of prostate cancer. Date: Tuesday, May 27, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Kilian Pohl, PhD: Professor of Psychiatry & Behavioral Sciences and, by courtesy, Electrical Engineering, Stanford University Title: Crafting Machine Learning Models for Neuroscience Discovery About: Dr. Pohl is a Professor of Psychiatry and Behavioral Sciences and, by courtesy, of Electrical Engineering, and the Director of the Computational Neuroscience Laboratory (CNSLab) at Stanford University. The focus of his laboratory is to advance computational neuroscience in identifying biomedical phenotypes that enhance personalized medicine toward the diagnosis and prevention of psychiatric disorders from childhood to old age. The CNSLab identifies phenotypes by coupling findings from unbiased, machine learning-based searches across highly dimensional biological, cognitive, neuroimaging, and behavioral data with insightful interpretations by Dr. Pohl’s clinical collaborators. Dr. Pohl is the principal investigator on awards from Stanford’s Institute for Human-Centered Artificial Intelligence and the National Institute of Health (NIH). Before joining Stanford, Dr. Pohl received his Ph.D. in computer science from the Massachusetts Institute of Technology and was faculty at Harvard, IBM Research, the University of Pennsylvania, and SRI International. Date: Tuesday, April 22, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Sophia Wang, MD, MS: Assistant Professor of Ophthalmology, Stanford University Title: Challenges and Opportunities for AI in Eye Care About: Dr. Wang is an ophthalmologist specializing in glaucoma and a clinician scientist in the Department of Ophthalmology at Stanford. Her research focuses on developing and evaluating artificial intelligence methods to predict ophthalmic outcomes using electronic health records. Dr Wang's work on developing algorithms to predict glaucoma progression and evaluating the fairness and generalizability of EHR models is funded by the NIH, the American Glaucoma Society, and Research to Prevent Blindness. Date: Tuesday, March 25, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Register Here Speaker: Angela Aristidou, PhD: Professor, University College London; Faculty Fellow at the Stanford Digital Economy Lab Title: AI Deployment in Real-World Clinical Settings About: Professor Angela Aristidou speaks, writes, and advises about the real-life deployment of artificial intelligence tools for public good. Her research spans the contexts of health, higher education, nonprofit, and humanitarian aid, in the UK, United States, Canada, and several Asian countries. Her current work has been honored through a Stanford CASBS Award and a generous UK Research Innovation Award. She specializes in strategy and entrepreneurship at University College London’s School of Management, is a Fellow at the Stanford Digital Economy Lab and the Stanford Institute for Human-Centered AI, and holds degrees from Cambridge and Harvard. Moderator: Sneha Shah Jain: MD, MBA: Clinical Assistant Professor of Medicine, Division of Cardiovascular Medicine, Stanford University About: Sneha S. Jain is a Clinical Assistant Professor of Medicine in the Division of Cardiovascular Medicine. Her research focuses on the development and responsible evaluation of AI tools to augment healthcare delivery and improve patient outcomes. She works with the Stanford Center for Clinical Research and the Data Science Team at Stanford to deploy and prospectively evaluate AI solutions across the healthcare enterprise. Sneha S. Jain received her BS in Economics from Duke University, MD from the Johns Hopkins School of Medicine, and her MBA from Harvard Business School. She completed internal medicine residency at Columbia/NewYork-Presbyterian, and cardiovascular medicine fellowship at Stanford University. Date: Tuesday, February 25, 2025 Time: 9:00-10:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Watch Recording Here Speaker: Nigam Shah, MBBS, PhD: Professor of Medicine, and of Biomedical Data Science; Chief Data Scientist, Stanford Healthcare; Associate Dean for Research, School of Medicine; Associate Director, Stanford Center for Biomedical Informatics Research, Stanford University Title: Responsible AI at Stanford Healthcare About: Dr. Shah is Professor of Medicine at Stanford University, and Chief Data Scientist for Stanford Health Care. His research is focused on bringing AI into clinical use, safely, ethically and cost-effectively. Dr. Shah is an inventor on eight patents, has authored over 300 scientific publications, and has co-founded three companies. Dr. Shah was inducted into the American College of Medical Informatics (ACMI) in 2015 and the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University. Grand Rounds Launch with Dr. Curtis Langlotz Date: Tuesday, January 28, 2025 Time: 8:00-9:00am PT Format: Live webinar presentation and Q&A Registration: Open to the Stanford and AIMI affiliate community Watch Recording Here Speaker: Curtis Langlotz, MD, PhD: Director, Center for Artificial Intelligence in Medicine & Imaging; Professor of Radiology, Medicine, and Biomedical Data Science, and Senior Associate Vice Provost for Research, Stanford University Title: Developing Clinically Useful AI for Radiology About: Dr. Langlotz is Professor of Radiology, Medicine, and Biomedical Data Science, and Senior Associate Vice Provost for Research at Stanford University. His NIH-funded laboratory develops machine learning methods to improve the accuracy and efficiency of medical image interpretation. He also serves as Senior Fellow at Stanford’s Institute for Human-Centered Artificial Intelligence and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports over 200 faculty at Stanford who pursue interdisciplinary machine learning research to improve clinical care. CME Credit Information Each session is 1.0 credits: AMA PRA Category 1 Credits™ (1.00 hours); Non-Physician Participation Credit (1.00 hours). Credit can only be recorded via text during or up to 24 hours after the session. You must attend the live session to claim credit.	common_crawl_stanford.edu_394
Past AIMI NextGen Tech Talk Episodes Main content start Past AIMI NextGen Tech Talk Episodes - Past Event High School Education AIMI NextGen Tech Talks (Episode 7): Charting A Path Towards an Equitable Data Ecosystem ft. Drs. Alaa Youssef, PhD and Madelena Ng, DrPH -This event has already occurred. - Past Event High School Education AIMI NextGen Tech Talks (Episode 6): AI In Medicine: ChatGPT and Beyond - Insights from a Physician Data-Scientist -This event has already occurred.Virtual - Past Event High School Education AIMI NextGen Tech Talks (Episode 5): Nishith Khandwala, MS -This event has already occurred. - Past Event High School Education AIMI NextGen Tech Talks (Episode 4): Dr. Jessica Mega, MD, MPH -This event has already occurred.Virtual - Past Event High School Education AIMI NextGen Tech Talks (Episode 3): Machine Learning in Medical Imaging -This event has already occurred. - Past Event High School Education AIMI NextGen Tech Talks (Episode 2): AI in Emergency Medicine -This event has already occurred.Virtual - Past Event High School Education AIMI NextGen Tech Talks (Episode 1): Launch Event ft. Dr. Curt Langlotz, MD, PhD -*This event has already occurred.Virtual	common_crawl_stanford.edu_395
AIMI Pediatric Symposium 2025 Machine learning and artificial intelligence are transforming medicine, driving innovation that improves health for all. Now in its sixth year, the AIMI Symposium is our flagship annual meeting, showcasing groundbreaking research, real-world clinical applications, and critical discussions on translation and societal impact. This gathering brings together researchers, clinicians, policymakers, and others dedicated to advancing AI in healthcare. Through dynamic sessions and collaborative dialogue, we tackle key challenges, bridge gaps, and champion evidence-based strategies to enhance patient care. New this year, we’re excited to introduce the AIMI Pediatric Symposium, a dedicated morning focused on the role of AI in pediatric care—exploring innovations that support the unique needs of children and young patients. Join us to explore the latest breakthroughs, engage with leading experts, and shape the future of AI in pediatric medicine. Event Details AIMI Pediatric Symposium June 4, 2025 (Wednesday) 8:30am - 1:00pm Pacific Time In person and online, free and open to all The button above is the registration link for the AIMI Pediatric Symposium (June 4). If you're interested in registering for the AIMI Symposium (June 3), please see here.	common_crawl_stanford.edu_396
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Main content start Professor Brian Hargreaves Professor Of Radiology (Radiological Sciences Laboratory) And, By Courtesy, Of Electrical Engineering And Of Bioengineering View Full Stanford Profile Back to Top	common_crawl_stanford.edu_397
Curtis Langlotz, MD, PhD Dr. Langlotz is Professor of Radiology and Biomedical Informatics and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports outstanding interdisciplinary artificial intelligence research that optimizes how clinical images are used to promote health. As Associate Chair for Information Systems and a Medical Informatics Director for Stanford Health Care, he is also responsible for the computer technology that supports the Stanford Radiology practice, including 7 million imaging studies that occupy 0.7 petabytes of storage. Dr. Langlotz’s laboratory investigates the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. He has led many national and international efforts to improve the quality of radiology communication, including the RadLex™ terminology standard, the RadLex™ Playbook of radiology exam codes, the RSNA report template library, and a technical standard for communication of radiology templates. He has published over 100 scholarly articles, and is author of the recent book “The Radiology Report: A Guide to Thoughtful Communication for Radiologists and Other Medical Professionals”. Raised in St. Paul, Minnesota, Dr. Langlotz received his undergraduate degree in Human Biology, masters in Computer Science, MD in Medicine, and PhD in Medical Information Science, all from Stanford University. He is a founder and past president of the Radiology Alliance for Health Services Research (RAHSR), and has served as president of the Society for Imaging Informatics in Medicine (SIIM), and the College of SIIM Fellows. He is a former board member of the Association of University Radiologists (AUR), the American Medical Informatics Association (AMIA) and the Society for Medical Decision Making (SMDM). He currently serves on the Board of Directors of the Radiological Society of North America (RSNA) as Liaison for Information Technology. He is a recipient of the Lee B. Lusted Research Prize from the Society of Medical Decision Making and the Career Achievement Award from the Radiology Alliance for Health Services Research. He has founded three healthcare information technology companies, most recently Montage Healthcare Solutions, which was acquired by Nuance Communications in 2016.	common_crawl_stanford.edu_398
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Main content start Graduate Student, Computer Science David Eng Back to Top	common_crawl_stanford.edu_399
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Main content start Professor Garry Gold Professor of Radiology (Musculoskeletal Imaging) View Full Stanford Profile Back to Top	common_crawl_stanford.edu_400
Glenn Cohen Prof. Cohen is one of the world's leading experts on the intersection of bioethics (sometimes also called "medical ethics") and the law, as well as health law. He also teaches civil procedure. From Seoul to Krakow to Vancouver, Professor Cohen has spoken at legal, medical, and industry conferences around the world and his work has appeared in or been covered on PBS, NPR, ABC, CNN, MSNBC, Mother Jones, the New York Times, the New Republic, the Boston Globe, and several other media venues. He was the youngest professor on the faculty at Harvard Law School (tenured or untenured) both when he joined the faculty in 2008 (at age 29) and when he was tenured as a full professor in 2013 (at age 34), though not the youngest in history. Prof. Cohen's current projects relate to big data, health information technologies, mobile health, reproduction/reproductive technology, research ethics, organ transplantation, rationing in law and medicine, health policy, FDA law, translational medicine, and to medical tourism – the travel of patients who are residents of one country, the "home country," to another country, the "destination country," for medical treatment. He is the author of more than 150 articles and chapters and his award-winning work has appeared in leading legal (including the Stanford, Cornell, and Southern California Law Reviews), medical (including the New England Journal of Medicine, JAMA), bioethics (including the American Journal of Bioethics, the Hastings Center Report), scientific (Science, Cell, Nature Reviews Genetics) and public health (the American Journal of Public Health) journals, as well as Op-Eds in the New York Times and Washington Post. Cohen is the author, co-author, editor, or co-editor of more than 15 books. They include: Readings in Comparative Health Law and Bioethics (Carolina Academic Press, 2020) Disability, Health, Law, and Bioethics (Cambridge University Press, 2020); Transparency in Health and Health Care in the United States (Cambridge University Press, 2019); Health Care Law and Ethics (Aspen, 2018); Big Data, Health Law, and Bioethics (Cambridge University Press, 2018); Law, Religion, and Health in the United States (Cambridge University Press, 2017); Specimen Science (MIT Press, 2017); Nudging Health: Health Law and Behavioral Economics (John Hopkins University Press, 2016) The Oxford Handbook of U.S. Health Care Law (Oxford University Press, 2016); FDA in the Twenty-First Century: The Challenges of Regulating Drugs and New Technologies (Columbia University Press, 2015); Identified Versus Statistical Lives: An Interdisciplinary Perspective (Oxford University Press, 2015); Patients with Passports: Medical Tourism, Law, and Ethics (Oxford University Press, 2014); Human Subjects Research Regulation: Perspectives on the Future (MIT Press, 2014); The Globalization of Health Care: Legal and Ethical Issues (Oxford University Press, 2013). For his law school teaching, he was awarded the HLS Student Government Teaching and Advising Award in 2017. He also sometimes teaches courses at Harvard College and Harvard Medical School. For the public, he created the free online Harvard X class Bioethics: The Law, Medicine, and Ethics of Reproductive Technologies and Genetics, which was nominated by Harvard for the Japan Prize. More than 97,000 students have taken the course so far. You can also watch his Tedx talk, Are There Non-Human Persons? Are There Non-Person Humans? He is also the faculty lead on Zero-L, an online course to help law students transition to law school that is now being used by more than half of all U.S. law schools. Prior to becoming a professor, he served as a law clerk to Judge Michael Boudin of the U.S. Court of Appeals for the First Circuit and as a lawyer for U.S. Department of Justice, Civil Division, Appellate Staff, where he handled litigation in the Courts of Appeals and (in conjunction with the Solicitor General’s Office) in the U.S. Supreme Court. In his spare time (where he can find any!) he still litigates, having authored an amicus brief in the U.S. Supreme Court for leading gene scientist Eric Lander in Association of Molecular Pathology v. Myriad, concerning whether human genes are patent-eligible subject matter, a brief that was extensively discussed by the Justices at oral argument. Most recently he submitted an amicus brief to the U.S. Supreme Court in Whole Women's Health v. Hellerstedt (the Texas abortion case, on behalf of himself, Melissa Murray, and B. Jessie Hill). Cohen was selected as a Radcliffe Institute Fellow for the 2012-2013 year and by the Greenwall Foundation to receive a Faculty Scholar Award in Bioethics. He is also a Fellow at the Hastings Center, the leading bioethics think tank in the United States as well as being a fellow of the Pierre Elliot Trudeau Foundation. He leads the Project on Precision Medicine, Artificial Intelligence, and the Law (PMAIL), which is part of the larger Centre for Advanced Studies in Biomedical Innovation Law (CeBIL). He co-leads the Regulatory Foundations, Ethics, and Law Program of Harvard Catalyst \| The Harvard Clinical and Translational Science Center program. He also leads the Project on Precision Medicine, Artificial Intelligence, and the Law (PMAIL). He previously served as one of the key co-investigators on the multi-million dollar Football Players Health Study at Harvard which is committed to improving the health of NFL players (for more on this work click here). He is also one of three editors-in-chief of the Journal of Law and the Biosciences, a peer-reviewed journal published by Oxford University Press, and serves on the editorial board for the American Journal of Bioethics. He served on the Steering Committee for Ethics for the Canadian Institutes of Health Research (CIHR), the Canadian counterpart to the NIH, and the Ethics Committee for the American College of Obstetricians and Gynecologists (ACOG). He currently serves on the Ethics Committee of the U.S. Organ Procurement and Transplantation Network (OPTN).	common_crawl_stanford.edu_401
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Main content start Assistant Professor Jonathan Chen Assistant Professor Of Medicine (Biomedical Informatics) View Full Stanford Profile Back to Top	common_crawl_stanford.edu_402
Julia Adler-Milstein, PhD Dr. Julia Adler-Milstein is a Professor of Medicine, Chief of the Division of Clinical Informatics & Digital Transformation, and Director of the Center for Clinical Informatics & Improvement Research (CLIIR).Dr. Adler-Milstein is a leading researcher who has examined policies and organizational strategies that enable effective use of electronic health records, promote interoperability, and integrate AI. She is also an expert in EHR audit log data and its application to studying clinician behavior. Her research – used by researchers, health systems, and policymakers – identifies obstacles to progress and ways to overcome them.She has published over 300 influential papers, testified before the US Senate Health, Education, Labor and Pensions Committee, is a member of the National Academy of Medicine, been named one of the top 10 influential women in health IT, and won numerous awards, including the New Investigator Award from the American Medical Informatics Association and the Alice S. Hersh New Investigator Award from AcademyHealth. Dr. Adler-Milstein holds a PhD in Health Policy from Harvard and became the inaugural Chief of the Division of Clinical Informatics and Digital Transformation in 2023.	common_crawl_stanford.edu_403
Main content start Vice President, Chief Technology Officer and Director of Advanced Imaging at Radnet Inc. Lawrence Tanenbaum Dr. Tanenbaum is currently Vice President, Chief Technology Officer and Director of Advanced Imaging at Radnet Inc. (since 2015), having come from Icahn School of Medicine at Mount Sinai in New York where he attended in Neuroradiology and served as an Associate Professor of Radiology, Director of MRI, CT and Outpatient / Advanced Imaging Development since 2008. Prior to that he spent over 20 years in the private practice of Radiology at the JFK Medical Center / New Jersey Neuroscience Institute as Director of MRI, CT and Neuroradiology. Dr. Tanenbaum is a senior member of the American Society of Neuroradiology, and long-term member of the Radiological Society of North America. He is a past President of the Eastern Society of Neuroradiology, and the national Clinical Magnetic Resonance Imaging Society and former Editor in Chief of their Journal Vision. He is a member of the Roster of Distinguished Scientific Advisors of the RSNA as well as several panels and committees of the American College of Radiology including the Expert Panel on Neuroimaging and the CPI / Neuroradiology Expert Review Panel. Dr. Tanenbaum is a member of the editorial boards of several journals and educational organizations and is the Associate Editor for Artificial Intelligence of Applied Radiology. Dr. Tanenbaum is a long-term collaborator with the medical imaging industry and chairs several advisory boards (OEM, pharma, and AI). He has interests in developing applications of AI and machine learning, contrast agents, MR, CT and advanced rendering. Dr. Tanenbaum is passionate about advancing the clinical practice of medicine focusing on patient centric care, efficiency, radiation dose and physiologic imaging. He is an active educator with interests in advanced imaging and innovative value-adding applications in the spine and brain. He has authored over 100 scholarly and peer reviewed articles which have been cited over 1000 times, continues to chair educational and academic meetings and has delivered close to 2000 invited lectures around the world.	common_crawl_stanford.edu_404
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Main content start Radiologist Melissa Davis Back to Top	common_crawl_stanford.edu_405
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Main content start Assistant Professor Nima Aghaeepour Assistant Professor (Research) Of Anesthesiology, Perioperative And Pain Medicine (Adult MSD) And, By Courtesy, Of Biomedical Data Science And Of Pediatrics (Neonatology) View Full Stanford Profile Back to Top	common_crawl_stanford.edu_406
Main content start Associate Chief Medical Officer for Clinical Artificial Intelligence at Radiology Partners Nina Kottler Dr. Nina Kottler has been a practicing radiologist for over 15 years. With her clinical experience combined with a graduate degree in applied mathematics and optimization theory, she has been using technological innovation to improve quality and drive value in radiology. As the first radiologist to join Radiology Partners, Dr. Kottler has held multiple leadership positions within the practice. Currently, she is the associate Chief Medical Officer for Clinical AI, serving on RP’s Innovation Steering Committee and Working groups and leading the Data Science and Analytics division of the Clinical Value Team. As a VP of Clinical Operations, Dr. Kottler also developed and practices in Radiology Partners’ remote imaging division, serves internally on their AI, IT, and Culture & Leadership support boards, and runs the Education and Development affinity group for RP’s Belonging committee. Externally Dr. Kottler serves on the following committees: ACR Metrics Committee, ACR Quality, and Safety Conference Planning Committee, SIIM Machine Learning Committee, SIIM Program Committee (Scientific Abstract Reviewer), RSNA Educational Exhibits Committee (Radiology Informatics Subcommittee), and the RAD= Steering Committee. In 2018, Dr. Kottler received the Trailblazer Award – an award recognizing a pioneering female leader in the field of imaging informatics.	common_crawl_stanford.edu_407
Skip to main content Skip to secondary navigation Stanford University (link is external) Center for Artificial Intelligence in Medicine & Imaging Search this site Submit Search Menu About Mission People Leadership Faculty Staff Director's Welcome AIMI Impact Report Research Focal Areas Grant Programs AIMI Seed Grants AIMI Trainee Travel Grants AIMI-HAI Partnership Grants AWS Cloud Credit Program Google Cloud Credit Program AIMI Funded Projects AIMI Seed Grant Projects AIMI-HAI Partnership Projects AWS Cloud Credit Projects Google Cloud Credit Projects AI for Healthcare Bootcamp Education AIMI Grand Rounds 2025 AIMI Grand Rounds January High School Students Summer Research Internship Summer Health AI Bootcamp NextGen Tech Talks Past AIMI NextGen Tech Talk Episodes Student Spotlight Executive Education Resources Shared Datasets Commercial Use of AIMI Datasets AIMI Dataset Index Office Hours Data Sharing Tools Software Tools MD.ai MD.ai Access Industry Industry Affiliate Program Sponsored Research Commercial Use of AIMI Datasets Events Upcoming Events Past Events AIMI Grand Rounds AIMI Symposium 2025 Agenda Registration Student & Trainee Registration Event Info Planning Committee AIMI Pediatric Symposium 2025 Agenda Stanford Health AI Week AI+HEALTH 2024 FAQs Industry Spotlight Session Engage Giving to AIMI Affiliate Faculty Postdoctoral Scholars Students Summer Program Student Leads Subscribe Main content start Professor Wendy DeMartini Professor Of Radiology (Breast Imaging) View Full Stanford Profile Back to Top	common_crawl_stanford.edu_408
AIMI-HAI Partnership Grants The AIMI-HAI Partnership Grant is designed to fund new and ambitious ideas that reimagine artificial intelligence in healthcare, using real clinical data sets, with near term clinical applications. Visit the Call for Proposals page to explore criteria and eligibility for our last award cycle. The next call for proposals is planned for mid-2025. If you have any questions, please contact hai-grants@lists.stanford.edu. 2023-2025 Projects: CBT-AI Companion: An Application for Improving Mental Health Treatment PI: Johannes Eichstaedt Abstract: We seek to develop a "CBT-AI Companion," an LLM-based application designed to enhance mental health treatment. Addressing the prevalent but undertreated issues of depression and anxiety, the project aims to increase the effectiveness of psychotherapy, particularly cognitive behavioral therapy (CBT). Traditional methods often see low compliance in practicing therapy skills, which are crucial for treatment success. The project leverages large language models (LLMs) to support patients in practicing cognitive and behavioral skills, offering immediate feedback and personalized experiences based on the patient’s context and stressors. This approach is expected to improve clinical outcomes due to a stronger engagement in skill practice. Bridging the Modality Gap: Diffusion Implicit Bridges for Inter-Modality Medical Image Translation PI: Sergios Gatidis Abstract: Modern machine learning algorithms for medical image analysis perform well on tasks that are limited to a single imaging modality or contrast. However, these algorithms face limitations when processing imaging data that includes different modalities. In this project, we aim to address this limitation by developing machine learning algorithms that can translate between different medical imaging modalities. We will base our work on diffusion models - a class of machine learning models successfully used for the generation and analysis of image data in various domains. With this project, we expect to open new possibilities in machine learning-based processing and analysis of medical imaging data and to build algorithms that are accessible for a broader range of clinical situations and a larger number of patient groups. Development of AI-Enabled Quadruped Robots (Pupper) for Improved Pediatric Patient Experience and Healthcare Outcomes PI: Karen Liu This project focuses on Pupper, an AI-enabled robotic dog developed at Stanford, aimed at improving the hospital experience for pediatric patients facing social isolation, depression, and/or anxiety. Unlike traditional quadrupeds, Pupper is approachable, cost-effective, and safe, making it well-suited for child interaction. It offers an engaging alternative to conventional sedation methods, potentially reducing healthcare costs and medication risks. Pupper, with its computer vision and agility capabilities, has shown promise to also serve as physical therapy motivator and emotional support. This research will progress along two parallel paths: technical enhancement of Pupper, including AI advancements like computer vision, autonomous gait, and speech processing, and clinical studies assessing Pupper’s impact in pediatric care. These studies will focus on mitigating social isolation, and reducing anxiety and/or depression, and facilitating physical therapy participation among hospitalized children. Developing AI for Automated Skill Assessment in Open Surgical Procedures PI: Serena Yeung Abstract: Surgical interventions are a major form of treatment in modern healthcare, with open surgical procedures being the dominant form of surgery worldwide. Surgeon skill is a key factor affecting patient outcome, yet current methods for assessing skill are primarily qualitative and difficult to scale. Our project endeavors to make strides in developing AI as an engine for automated skill assessment in open surgical procedures. Whereas most prior work has focused on AI for laparoscopic surgical procedures, open procedures present more challenges due to the larger and more complex field of view. We will develop methods for providing complementary forms of feedback from surgical video, including kinematics analysis and action quality assessment through video question answering. Finally, we will evaluate the utility of our AI methods through pilot studies with surgical trainees. Our project aims to demonstrate the feasibility of AI in contributing quantitative and scalable skill assessment and feedback in surgical education. 2021-2023 Projects Developing Artificial Physician Intelligence for the Early Detection of STEMI: Closing an Emergency Care Clinical Practice Gap PI: Maame Yaa Yiadom Abstract: This project will advance the development of artificial intelligence (AI) to identify patients at risk for ST-segment elevation myocardial infarction (STEMI). Screening patients upon arrival in an emergency department is done to identify those who potentially have this most severe form of a heart attack. Diagnosing STEMI needs to occur within 10 minutes. The project team will improve current practice by pursuing the integration of AI, designed to replicate physician decision making, into STEMI screening. The execution of the project brings together 3 areas of expertise – emergency cardiovascular care, clinical informatics and predictive modeling analytics. The first phase of work will quantify the value of socio-demographic diversity characteristics in augmenting the sensitivity and precision of risk prediction. In addition, the team will silently pilot the screening model as physician AI within the Stanford Adult Hospital’s electronic health record (EHR) system. The AI will use live clinical care data for 6 months. The team will then measure the timeliness of the physician AI’s decision making, and the effectiveness of risk prediction in comparison to actual clinical care screening. This work explores the feasibility of a mechanistic approach to translating physician AI into the clinical environment in order to improve timely diagnosis for a time-sensitive medical condition. Digital Machine Learning Prediction Models for High-Value Oncology Diagnostic Testing PI: Henning Stehr Abstract: Comprehensive genome profiling of tumor specimens is an important new instrument in the diagnosis and treatment of cancer. With an increasing number of available molecular testing options, it can be difficult to choose the most relevant tests from the available test menu. Machine learning tools promise to be a new and important source of information for oncologists to make the best choice for their patients. For the Heme-STAMP tumor profiling test as an example application, we are developing a prediction tool that uses patient-specific data available in the electronic health record to predict how likely the molecular test will yield new and actionable results. This information will be presented in real-time to the ordering provider so that it can be used to select the most relevant test for the patient. Opportunistic CT Imaging for Early Detection of Chronic Disorders: Multicenter Retrospective Validation & Prospective Deployment PI: Akshay Chaudhari Abstract: Early detection of chronic disorders can improve population-level quality of life, longevity, and health care costs. While multiple screening tests for chronic disorders exist, these can have low compliance, add to healthcare costs, and be insensitive to early-stage diseases when interventions may be most effective. To address this, we will implement a solution for diagnosing ischemic heart disease, diabetes mellitus, and osteoporosis, using abdominal computed tomography (CT) scans that have already been acquired for additional reasons. Such CT scans can provide salient biomarkers such as the distribution of fat and muscle within the body, vascular calcifications, and bone mineral density measures, all of which are biomarkers of future disease activity. We will combine these images with a patient’s medical record and build explainable models for communicating model risks to both clinicians and patients. This high-value paradigm of opportunistic analysis using already-acquired imaging has the potential to improve patient outcomes without requiring additional testing or adding to the already burgeoning costs of healthcare. Place Matters: The Streetscape Environment and Health among African Americans PI: Michelle Odden Abstract: Substantial literature demonstrates the significance of the human-made environment on key health behaviors and outcomes. However, most studies have been based on large-scale geographic (GIS) measures, which typically do not represent the local context in which individuals regularly interact with their environments. Evidence has emerged that the streetscape can affect health outcomes and disparities. Traditional streetscape audits require researchers to walk through an environment and manually classify features; however, this approach is time-consuming and relies on accurate and reliable human judgment. The emergence of widespread maps that feature panoramas of the environment (e.g., Google Street View) offers unprecedented opportunity for measuring streetscape features at the perspective from which individuals interact with their environment. Coupled with deep learning methods to extract features, this approach will overcome the limitations of the traditional streetscape audit. The overarching hypothesis of this work is that the presence of positive streetscape features can help enhance health. These types of features, such as lighting, safe pedestrian paths, and greenspace, may be especially important in under-resourced communities with high levels of health disparities. The proposed research will be conducted in collaboration with a population-derived cohort of African Americans living in the Deep South. Employing innovative human-centered artificial intelligence and computer vision methods, we will evaluate whether patterns of streetscape features are associated with physical activity, well-being, and chronic disease, independent of traditional risk factors and GIS-based measures. Standardized Therapy Response Assessments of Pediatric Cancers PI: Heike E. Daldrup Abstract: Imaging tests are essential for diagnosing cancers in children and for monitoring tumor response to therapy. New technologies enable simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) images, which allows for “one stop” cancer staging. However, the interpretation of 30,000 – 50,000 images generated with the PET/MRI technology is time consuming and prone to variability from one observer to another. In children with lymphoma, tumor response to chemotherapy is typically expressed by a 5-point score (the “Deauville score”) that describes the tumor signal on PET scans as being higher or lower compared to the signal of major blood vessels and the liver. Human observers tend to show limited reproducibility of intermediate scores of 2, 3 or 4. We propose to solve this problem by developing deep convolutional neural networks (Deep-CNN) that can accelerate and standardize pediatric PET/MR image data interpretation. The goal of our project is to develop a Deep-CNN for standardized Deauville scoring of lymphomas in children. We hypothesize that Deep-CNN can significantly (> 50%) speed up PET image interpretation times and improve the reproducibility of Deauville score assessments. To the best of our knowledge, this is the first attempt to apply Deep-CNN to interpretations of pediatric cancer imaging studies. Results will be readily translatable to the clinic and thereby, will have major and broad health care impact. Despite the obvious need of accelerated medical diagnoses for children with cancer, no current strategy has yet employed the use of Deep-CNNs to speed up and reduce variability in image data interpretation of children with cancer. This is because Deep-CNNs need to be trained on large data sets to achieve satisfactory performance. Since pediatric cancers are more rare than adult cancers and PET/MRI technologies are relatively new, there are limited PET/MRI data of children available to date. We are in a unique position to address this problem because we have established a centralized image registry with PET/MRI data of pediatric cancer patients from five major children’s hospitals. This will enable us to train and validate a Deep-CNN for therapy response assessments of pediatric cancers. Once established, our Deep-CNN can be made available to other institutions and cross-trained for other tumor types and adult patients. Self-service Data Science in the EHR with Multimodal Patient Embeddings PI: Keith Morse Abstract: Analysing electronic health records (EHR) with machine learning holds great promise in tackling key problems in healthcare. However the scale, complexity, and heterogeneity of EHR data creates challenges for integrating these data into machine learning models. Current data science tools for EHRs largely focus on count-based models using structured data (e.g., medical codes, labs, demographics) and fail to capture critical information found in text and images. Moreover cohort sizes are typically small, failing to capture generalizable signals found across larger-scale patient populations. The inability to easily create feature representations that contextualize patient state and capture the full richness of EHR data directly impacts almost all clinical data science applications. Building on our prior work training foundation models using structured data from the entire Stanford Medicine patient population, we will develop a multimodal patient representation learning framework which combines structured EHR codes, clinical notes, and images. We will evaluate classifiers trained with our embeddings on three cohorts: pediatric sepsis patients presenting within 3 days of admission; patients diagnosed with pulmonary embolism, and CheXpert. This foundation model will be integrated into our patient search engine and cohort analysis tool, the Advanced Cohort Engine (ACE). We hypothesize that patient embeddings generated with multimodal data will improve classification performance across a range of clinical tasks, drive new insights via latent subclass analyses, and enable new modes of error analysis for clinical researchers. All of our code will be released as open source software and include guidance on using GCP infrastructure to train custom cohorts and include estimates for training time, cloud costs, and carbon footprint.	common_crawl_stanford.edu_409
Deep Learning for Computer Vision Below is a list of active and ongoing projects from our lab group members. To learn more, click on the project links otherwise reach out to us via email. Automatically Staging Osteoarthritis from X-rays and MRIs Osteoarthritis (OA) is a leading cause of disability in older adults. Progress towards the development of disease modifying drugs and rehabilitation strategies is hindered by many factors, including lack of objective and accurate tools to assess disease progression. Large observational studies have collected yearly imaging data on thousands of patients for nearly a decade, but reliance on radiologists to process these data has currently stalled the OA research community from generating new insights on the natural progression of the disease. Rapid training set construction with technologies like Coral is crucial to facilitating these insights. The initial goal of this project is to develop a framework for automatically and reliably staging osteoarthritis based on knee Xrays. The follow-up goal is to automatically assess knee joint abnormalities, including bone marrow lesions, from MRI data. Lastly, automatic segmentation of specific structures in the knee (e.g., cartilage) from MRI scans would be the most impactful contribution. Cross-Modal Weak Supervision: Leveraging Text Data at Training Time to Train Image Classifiers More Efficiently Arguably the largest development bottleneck in machine learning today is getting labeled training data. One promising direction is the use of weaker supervision that is noisier and lower-quality, but can be provided more efficiently and at a higher level by domain experts and then denoised automatically. In one current project, Snorkel, users write labeling functions to express heuristics that can generate noisy labels. These labeling functions are often easy to write over text, but less so over images. However, in many important cases we have both images and text available at training time: for example, in radiology applications, we want to train an image classifier, but also have unstructured text reports available at training time. In this project, we are exploring how this text data can be used to help more easily provide weak supervision for the end image model. Learning to Compose Domain-Specific Transformations for Data Augmentation One of the cornerstone techniques used with deep learning in practice is data augmentation: transforming data points in class-preserving ways (e.g. rotations, small crops, etc) to artificially increase the size of labeled training sets. Data augmentation provides significant performance gains and can be viewed as a way for domain experts to easily inject knowledge about task- and domain-specific invariants. In work to date (see links above), we have explored methods for automatically learning data augmentation models given basic transformation operations provided by domain experts. In current work, we are exploring both the theoretical foundations of data augmentations and applications to medical imaging such as mammogram and histopathology image classification. Other Projects - Utilize machine vision techniques to classify de-identified chest radiographs for misplaced endotracheal tubes, central lines, and pneumothorax. - Develop a deep learning model that can accurately classify an imaging sequences according to modality, body region, imaging technique, imaging plane, phase and type of contrast, and MR pulse sequence. - Evaluate a convolutional neural network model that can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of current state-of-the-art feature-extraction-based automated bone age assessment models - Develop a deep-learning classifier for evaluating pediatric brain MRI - Use deep learning to predict "brain age" using MRI data - Investigate deep learning in "super human" imaging tasks including PE prediction on chest xrays and stroke detection on head CT - Develop a convolutional neural network model that can predict pathology/genomic information from imaging examinations in pediatric cancer - Using deep learning for rapid histopathology diagnosis in the operative setting - Deep learning to identify facial features from cross sectional imaging - Utilize a deep learning method for emergent imaging finding detection (multi-modality) - Investigate whether scanner-level deep learning models can improve detection at the time of image acquisition - Computer vision for CAD in FDG and bone scans - Automated fetal brain ultrasound diagnosis and evaluation with deep learning - Musculoskeletal tumor identification on plain films with histopathologcal confirmation with deep learning - Deep learning for imaging followup in clinical trials - Real-time detection and diagnosis of video cystoscopy with deep learning	common_crawl_stanford.edu_410
AIMI Meetings Research Meetings AIMI Research meetings feature updates from AIMI investigators and affiliates, as well as guests, on their current and ongoing work. Once a month (every Third Thursday), investigators conducting innovative experiments in healthcare AI are invited to present work-in-progress or completed work. Presenters receive feedback on their current research and hear discussion from the diverse experts in the AIMI community. If you are interested in presenting at an AIMI Research meeting, please email aimicenter@stanford.edu. Industry Trends Meetings Relationships between companies, faculty, and trainees provide all constituents with valuable insights on opportunities, problems, and solutions at the intersections of AI and health/medicine. Once a month (every second Thursday), our industry affiliates and associate fellows are invited to share about their work and for two-way exchange of insights and ideas with the AIMI community. If you are interested in presenting at an AIMI Industry Trends meeting, please sign-up here. Journal Club The AIMI Journal Club is an initiative to assemble diverse audiences and to discuss new advances in AI techniques for healthcare. Once a month (every second Thursday), authors who have published new innovative manuscripts in healthcare AI are invited to present their research studies in the AIMI Journal Club. This meeting is divided into two parts, where the first half of the meeting is dedicated to the author presentations while the second half is used as a time for discussion between the AIMI members and the invited authors. If there's an article that you would like considered for a future Journal Club, please submit it here.	common_crawl_stanford.edu_411
James Bishop has been awarded a Ruth L. Kirschstein National Research Service Award (NRSA) F32 Postdoctoral Fellowship! This competitive grant is awarded by the NIH on a national basis for highly promising postdoctoral candidates to support their potential to become productive, independent investigators in scientific health-related research fields. He will be working on drug uncaging technologies to alleviate chronic pain. Tommaso Di Ianni was awarded a School of Medicine Dean’s Postdoctoral Fellowship, a highly competitive fellowship to support young investigators in the first two years of their postdoctoral research training. Tommaso is working on designing ultrasound transducers that will enable behavioral studies in awake freely-moving animals, along with functional ultrasound to imaging brain activity in real time. Daivik Vyas has been rewarded a MedScholars Fellowship to conduct research on using ultrasound to modulate the glymphatic system for drug delivery. The MedScholars program provides funding for Stanford medical students to conduct research in basic, clinical, and translational settings. Jeff Wang has been rewarded a Stanford Interdisciplinary Graduate Fellowship (SIGF) to characterize whole-brain oscillatory, metabolic, and behavioral changes associated with localized ketamine uncaging. The SIGF is a competitive, university-wide award given to doctoral students engaged in interdisciplinary research in humanities, social sciences, basic sciences, and engineering. Congrats again to all! Here, we show that nanoparticle-mediated ultrasonic drug uncaging is generalizable to a wide range of hydrophobic drugs and also demonstrate the needed stability and drug loading for clinical translation. Given this wide range of drugs, we have the potential to potentially treat diseases as wide-ranging as cancer, neuropsychiatric disorders, and stroke. The paper can be found here: https://www.sciencedirect.com/science/article/pii/S0142961219301656?via%3Dihub.	common_crawl_stanford.edu_412
Airan Lab Research Publications People Opportunities Lab Fun Contact Us Intranet Airan Lab David-1 ‹ Return to Lab Fun Posted on September 10, 2023 by byu24 \| Comments Off on David-1 ← Previous Image Next Image → Comments are closed. Recent Posts Congratulations to Tommaso who will be starting his new position at UCSF Weill Institute for Neurosciences as Assistant Professor in the Department of Psychiatry and Behavioral Sciences with a joint appointment in the Department of Radiology and Biomedical Imaging! Congratulations to Jeff for matching into Anesthesiology at John Hopkins! Congrats Douglas Martin for receiving a 2019 RSNA Roentgen Resident/Fellow Research Award!!! Congratulations to our lab members who have recently received fellowships! Qian and Jason’s paper “Polymeric perfluorocarbon nanoemulsions are ultrasound-activated wireless drug infusion catheters” is now published in Biomaterials. Congrats to the authors! Recent Comments Archives April 2023 March 2023 July 2019 January 2019 November 2018 October 2018 September 2018 June 2018 March 2018 February 2018 November 2017 August 2017 July 2017 June 2017 Categories Uncategorized Meta Log in Entries feed Comments feed WordPress.org	common_crawl_stanford.edu_413
Airan Lab Research Publications People Opportunities Lab Fun Contact Us Intranet Airan Lab PXL_20230414_221300971 ‹ Return to Lab Fun Posted on April 21, 2023 by byu24 \| Comments Off on PXL_20230414_221300971 ← Previous Image Next Image → Comments are closed. Recent Posts Congratulations to Tommaso who will be starting his new position at UCSF Weill Institute for Neurosciences as Assistant Professor in the Department of Psychiatry and Behavioral Sciences with a joint appointment in the Department of Radiology and Biomedical Imaging! Congratulations to Jeff for matching into Anesthesiology at John Hopkins! Congrats Douglas Martin for receiving a 2019 RSNA Roentgen Resident/Fellow Research Award!!! Congratulations to our lab members who have recently received fellowships! Qian and Jason’s paper “Polymeric perfluorocarbon nanoemulsions are ultrasound-activated wireless drug infusion catheters” is now published in Biomaterials. Congrats to the authors! Recent Comments Archives April 2023 March 2023 July 2019 January 2019 November 2018 October 2018 September 2018 June 2018 March 2018 February 2018 November 2017 August 2017 July 2017 June 2017 Categories Uncategorized Meta Log in Entries feed Comments feed WordPress.org	common_crawl_stanford.edu_414
Airan Lab Research Publications People Opportunities Lab Fun Contact Us Intranet Airan Lab Yu_Brenda_Headshot ‹ Return to People Posted on May 20, 2020 by Mario Landeros \| Comments Off on Yu_Brenda_Headshot ← Previous Image Next Image → Comments are closed. Recent Posts Congratulations to Tommaso who will be starting his new position at UCSF Weill Institute for Neurosciences as Assistant Professor in the Department of Psychiatry and Behavioral Sciences with a joint appointment in the Department of Radiology and Biomedical Imaging! Congratulations to Jeff for matching into Anesthesiology at John Hopkins! Congrats Douglas Martin for receiving a 2019 RSNA Roentgen Resident/Fellow Research Award!!! Congratulations to our lab members who have recently received fellowships! Qian and Jason’s paper “Polymeric perfluorocarbon nanoemulsions are ultrasound-activated wireless drug infusion catheters” is now published in Biomaterials. Congrats to the authors! Recent Comments Archives April 2023 March 2023 July 2019 January 2019 November 2018 October 2018 September 2018 June 2018 March 2018 February 2018 November 2017 August 2017 July 2017 June 2017 Categories Uncategorized Meta Log in Entries feed Comments feed WordPress.org	common_crawl_stanford.edu_415
Our Mission Designing education systems that can produce people who can thrive in the era of Robotics and AI requires gathering top researchers from the fields of Design Thinking, Robotics, AI, Neurology, and Education to work together on the challenges faced. We want to use the Design Thinking methodology in the field of education, combined with Robotics/AI and neurology knowledge to reinvent our education systems. Design Thinking methodology, which has been used in many areas especially production systems, can also be used in redesigning education as well. A deep understanding of robotics and AI is necessary to design education systems that will produce people who will collaborate with robots instead of competing with robots in the near future. In addition, experts in the field of neurology are also needed as a focus on the cultivation of true human intelligence is a core dependency in the design of our future education systems. In short, our mission is to redesign education systems with our expertise in Design, Robotics, AI, Neurology, and Education.	common_crawl_stanford.edu_416
About Dramatic recent progress in both artificial intelligence and structural biology has created tremendous opportunities for using machine learning methods not only to predict three-dimensional structures of drug targets but also to design safer, more effective drugs. Since 2015, multiple research groups at Stanford have been developing machine learning methods to leverage structure for the design of both biologics and small-molecule therapeutics. Current research directions include prediction of ligand binding poses, affinities, functional effects, and off-target properties; virtual screening; generative models for drug candidates; methods to achieve selectivity; and design of antibodies to optimize their developability. The Artificial Intelligence for Structure-Based Drug Discovery program provides opportunities for exchange of ideas between Stanford researchers developing groundbreaking machine learning methods that leverage molecular structure and industry scientists who wish to apply such methods to bring better drugs to the market efficiently. In order to maximize the real-world impact of their research, Stanford researchers welcome input from industry partners—for example, on which problems to tackle or which software features to add. Industry partners also benefit through exposure to cutting-edge research, a forum to ask questions about deployment of algorithms and software, and opportunities to network with both Stanford researchers and other industry partners. The Team AISBDD includes multiple Stanford professors and their research groups. Founding faculty are: Prof. Ron Dror (Computer Science and, by courtesy, Molecular and Cellular Physiology and Structural Biology) Prof. Russ Altman (Bioengineering, Genetics, Biomedical Data Science, Medicine and, by courtesy, Computer Science) Prof. Possu Huang (Bioengineering) All three have extensive experience in machine learning for structural biology and drug discovery. Their work in this area includes structure prediction for proteins, RNA, and target-ligand complexes; protein design; virtual screening; atomic-level simulation to guide drug discovery; toxicity prediction; and effects of genetic variation on drug response. They collaborate with a wide variety of experimentalists and companies.	common_crawl_stanford.edu_417
Ron Dror Ron Dror is a Cheriton Family Professor of Computer Science in the Stanford Artificial Intelligence Lab. He is also affiliated with the departments of Structural Biology and of Molecular and Cellular Physiology, the Institute for Computational and Mathematical Engineering, Bio-X, ChEM-H, and the Biophysics and Biomedical Informatics Programs. Dr. Dror leads a research group that uses machine learning and molecular simulation to elucidate biomolecular structure, dynamics, and function and to guide the development of more effective medicines. He collaborates extensively with experimentalists in both academia and industry. Before moving to Stanford, he served as second-in-command of D. E. Shaw Research, a hundred-person company, having joined as its first hire. Dr. Dror earned a PhD in Electrical Engineering and Computer Science at MIT, where he developed machine learning methods for computer vision and genomics. He earned an MPhil in Biological Sciences as a Churchill Scholar at the University of Cambridge, as well as undergraduate degrees in Mathematics and in Electrical and Computer Engineering at Rice University, summa cum laude. He has published over 30 papers in Nature, Science, and Cell and has won several Gordon Bell Prizes and Best Paper Awards.	common_crawl_stanford.edu_418
Willis Lounge (219 Ayrshire Farm Ln) There will be BBQing, spikeballing, slacklining, volleyballing, picnicking, and much general spring enjoyment 🕺🕺. Stop by for a burger, a beer (or both, you shld come for both), or to just lay on a blanket in the grass and contemplate your life and the state of the universe, whatever works for you 🤸♀️ Do us a big favor and fill out this form (please 😁😁) so we have enough food to fuel your spikeball, volleyball, slackline and picnic blanket lounging endeavors. Currently enrolled students get one of our best benefits – discounted IKON passes! Please check out this doc for instructions on how to get your discount. Please remember we are busy students – getting your discount might take a week! (Updated 4/07/25 by MEO) Pay $25 dues via ASSU ePay. Ensure that the dropdown menu says you are submitting payment for "dues" and that you pay $25. If joining SAC is cost prohibitive, contact Doug (dxl@stanford.edu) for an exemption. Complete SAC's google form. This step is required to get on club's listserv and google drive. Previous DSE step is replaced by Common Adventure Agreement / Driver Authorization directly in our google form. You're done! Please give officers a week to verify membership and add you to the listserv/drive. We're busy students, too ;) March 19th, 7:30-9:30pm @ CEMEX Auditorium, Stanford University For the past two decades, Reel Rock has been sharing the stories of the climbing community, producing films that celebrate the human side behind this sport’s great adventures. The Reel Rock Film Tour premieres the best new climbing films to communities around the world, with 500 locations in 40 countries – and counting! Whether you are a climber or not, Reel Rock 18 will deliver a joyful dose of inspiration, heart, and humour. Produced by the award-winning team behind films such as The Dawn Wall and The Alpinist, Reel Rock will leave you inspired to get out and take on your own adventures. Trailer and film info here: https://reelrocktour.com/ Parking: Option 1 is to park in the underground lot at the Knight Management Center Garage , also at 655 Knight Way, Stanford, CA 94305 on the northeast side of the buildings. Option 2 is to park at the outdoor parking lot in front of the Arrillaga Gymnasium and Weight Room at 657 Campus Drive, Stanford, CA 94305. You can park in C and A lots after 4pm for free. * SAC Members: Use the email you signed up with as your promo code to unlock 50% off your tickets. Must apply the email as a promo code, signing in with the email alone will not reveal the discounted tickets. * Stanford Alpine Club is having its first practice Monday, September 30th from 7-10pm. The first hour will be an informational overview about the club, membership benefits, and some of the super cool things we have going on this year. After that, we will have our first Monday night practice, a SAC exclusive time in the Stanford climbing gym that happens every Monday. Both of these events are open to anyone interested in the club. This is a great chance to have your questions about the club answered and socialize with other new members. REMINDER: In order to rope climb, you’ll need to go through the standard belay check at the Stanford gym, and be RECERTIFIED each year. Please obtain this ahead of the first practice in the gym (Monday September 30) as we will not offer belay certifications during practice. Please refer to the Stanford AOERC Climbing website to schedule a lead belay certification. Monday Night Practices: Starting Monday, September 30th, we are resuming practices for members every Monday night from 9-11pm at the AOERC climbing wall! In case you’ve forgotten what practices are… they’re a great time to unplug from life, and come climb with your fellow SAC teammates! Find a new climbing partner, get that beta you’ve been gunning for, send your gym project, plan a trip. In order to rope climb, you’ll need to go through the standard belay check at the Stanford gym, and be RECERTIFIED each year. Please obtain this ahead of the first practice in the gym (Monday September 30) as we will not offer belay certifications during practice. Please refer to the Stanford AOERC Climbing website to schedule a lead belay certification. 2023-2024 Membership: Being a SAC member gives you access to Yosemite campsites, subsidized Common Adventure trips, IKON Pass discounts, Monday night practices at the Stanford climbing wall, member listserv, fun film screenings and socials. Head to the Get Involved tab to register! Signing up is super easy for both current and prospective members who are a part of the Stanford community. If you are a current SAC member, renew your membership by Friday, October 6, to ensure you stay on the roster without interruption. (Updated on 9/29/24 by HTB) Adventure Programs at Stanford is restarting their Climber Coffee Meetups from last year! Much like Yosemite, Joshua Tree, and other national parks with climbing, we are starting our own climber coffee meet ups the first Tuesday of each month from 8:00am – 10:00am in the Outdoor Center. This is a great time for folks to come together and talk climbing, trip planning, meet other climbers, and enjoy free coffee. This is open to anyone regardless of climbing experience. Also note, the climbing will is also open from 8-10 am on Thursday, so head to the AOERC and get both your climbing and your coffee fix at the same place! Please reach out to Dan, Climbing Coordinator (dearhart@stanford.edu) with questions. (Updated on 9/19/23 by Kevin Crust)	common_crawl_stanford.edu_419
Willis Lounge (219 Ayrshire Farm Ln) There will be BBQing, spikeballing, slacklining, volleyballing, picnicking, and much general spring enjoyment 🕺🕺. Stop by for a burger, a beer (or both, you shld come for both), or to just lay on a blanket in the grass and contemplate your life and the state of the universe, whatever works for you 🤸♀️ Do us a big favor and fill out this form (please 😁😁) so we have enough food to fuel your spikeball, volleyball, slackline and picnic blanket lounging endeavors. Currently enrolled students get one of our best benefits – discounted IKON passes! Please check out this doc for instructions on how to get your discount. Please remember we are busy students – getting your discount might take a week! (Updated 4/07/25 by MEO) Pay $25 dues via ASSU ePay. Ensure that the dropdown menu says you are submitting payment for "dues" and that you pay $25. If joining SAC is cost prohibitive, contact Doug (dxl@stanford.edu) for an exemption. Complete SAC's google form. This step is required to get on club's listserv and google drive. Previous DSE step is replaced by Common Adventure Agreement / Driver Authorization directly in our google form. You're done! Please give officers a week to verify membership and add you to the listserv/drive. We're busy students, too ;) March 19th, 7:30-9:30pm @ CEMEX Auditorium, Stanford University For the past two decades, Reel Rock has been sharing the stories of the climbing community, producing films that celebrate the human side behind this sport’s great adventures. The Reel Rock Film Tour premieres the best new climbing films to communities around the world, with 500 locations in 40 countries – and counting! Whether you are a climber or not, Reel Rock 18 will deliver a joyful dose of inspiration, heart, and humour. Produced by the award-winning team behind films such as The Dawn Wall and The Alpinist, Reel Rock will leave you inspired to get out and take on your own adventures. Trailer and film info here: https://reelrocktour.com/ Parking: Option 1 is to park in the underground lot at the Knight Management Center Garage , also at 655 Knight Way, Stanford, CA 94305 on the northeast side of the buildings. Option 2 is to park at the outdoor parking lot in front of the Arrillaga Gymnasium and Weight Room at 657 Campus Drive, Stanford, CA 94305. You can park in C and A lots after 4pm for free. * SAC Members: Use the email you signed up with as your promo code to unlock 50% off your tickets. Must apply the email as a promo code, signing in with the email alone will not reveal the discounted tickets. * Stanford Alpine Club is having its first practice Monday, September 30th from 7-10pm. The first hour will be an informational overview about the club, membership benefits, and some of the super cool things we have going on this year. After that, we will have our first Monday night practice, a SAC exclusive time in the Stanford climbing gym that happens every Monday. Both of these events are open to anyone interested in the club. This is a great chance to have your questions about the club answered and socialize with other new members. REMINDER: In order to rope climb, you’ll need to go through the standard belay check at the Stanford gym, and be RECERTIFIED each year. Please obtain this ahead of the first practice in the gym (Monday September 30) as we will not offer belay certifications during practice. Please refer to the Stanford AOERC Climbing website to schedule a lead belay certification. Monday Night Practices: Starting Monday, September 30th, we are resuming practices for members every Monday night from 9-11pm at the AOERC climbing wall! In case you’ve forgotten what practices are… they’re a great time to unplug from life, and come climb with your fellow SAC teammates! Find a new climbing partner, get that beta you’ve been gunning for, send your gym project, plan a trip. In order to rope climb, you’ll need to go through the standard belay check at the Stanford gym, and be RECERTIFIED each year. Please obtain this ahead of the first practice in the gym (Monday September 30) as we will not offer belay certifications during practice. Please refer to the Stanford AOERC Climbing website to schedule a lead belay certification. 2023-2024 Membership: Being a SAC member gives you access to Yosemite campsites, subsidized Common Adventure trips, IKON Pass discounts, Monday night practices at the Stanford climbing wall, member listserv, fun film screenings and socials. Head to the Get Involved tab to register! Signing up is super easy for both current and prospective members who are a part of the Stanford community. If you are a current SAC member, renew your membership by Friday, October 6, to ensure you stay on the roster without interruption. (Updated on 9/29/24 by HTB) Adventure Programs at Stanford is restarting their Climber Coffee Meetups from last year! Much like Yosemite, Joshua Tree, and other national parks with climbing, we are starting our own climber coffee meet ups the first Tuesday of each month from 8:00am – 10:00am in the Outdoor Center. This is a great time for folks to come together and talk climbing, trip planning, meet other climbers, and enjoy free coffee. This is open to anyone regardless of climbing experience. Also note, the climbing will is also open from 8-10 am on Thursday, so head to the AOERC and get both your climbing and your coffee fix at the same place! Please reach out to Dan, Climbing Coordinator (dearhart@stanford.edu) with questions. (Updated on 9/19/23 by Kevin Crust)	common_crawl_stanford.edu_420
Frances C. Arrillaga Alumni Center Conveniently located on the corner of Campus and Galvez, your alumni center is a space to enjoy during your time back on the Farm. Amenities From office services to convenient meeting spaces, the Frances C. Arrillaga Alumni Center has your on-campus needs covered. Munzer Business Center Monday–Friday, 8 a.m.–5 p.m. Need to take care of office-related needs or meet with colleagues? Do it all at the Munzer Business Center. Showers and day lockers are also available. Alumni may reserve one of two conference rooms for up to six people, two hours per day. Call (650) 736-0467 for more information. COHO Cafe at the Alumni Center Monday–Thursday, 8 a.m.–3 p.m. Friday, 9 a.m. - 2 p.m. SAA is excited to partner with COHO! Everything you know and love about the original COHO is now at the Alumni Center, featuring SAA’s craft coffee. Plus, COHO is available for catering! Stanford Alumni Association Bicycle Program Monday–Friday, 8:30 a.m.–4:30 p.m. Want to explore campus on wheels? Alumni can rent a bike—and one for a guest too! Windhover Contemplative Center Looking for serenity during your next trip to campus? Alumni may visit Windhover by obtaining a building access card from our Alumni Center (326 Galvez Street) front desk during business hours. Our Spaces Explore the various rooms at the Frances C. Arrillaga Alumni Center. The Living Room The Dwight Family Living Room features a large limestone fireplace, a Steinway grand piano, plush couches and plenty of space to relax, recharge and reconnect with fellow alums. Floor-to-ceiling windows bring the outside in and offer views of the gardens and reflecting pool. Franklin Fountain and Ford Alumni Gardens The Franklin Fountain and Reflecting Pool is the centerpiece of the award-winning Ford Alumni Gardens. Designed by landscape architect John Wang, the gardens include Palm Court, a courtyard with Mexican fan palms, fifty indigenous plant species, and a stone terrace. Bing Library The Bing Library is tucked away in the northeast corner of the business center. In this secluded refuge, you'll find bookshelves filled with historic Stanford tomes, books by alumni authors, and a selection of the day's newspapers and campus publications. Sink into a leather writer's chair, kick your feet up on an ottoman and enjoy the quiet. Behind the Building: Dedication and Donors The Frances C. Arrillaga Alumni Center is named for an alumna of courage, boundless enthusiasm and unfailing devotion to her alma mater, her community and her family. This building is a gift from Frances C. Arrillaga’s family—husband John, ’59, son John Jr., ’92, MBA ’98, and daughter Laura, ’92, MBA ’97, MA ’98, and MA ’99—and many other alumni who share her commitment to Stanford. Frances C. Arrillaga, MA ’64, MA ’65 Fran made many contributions to the university through her Stanford Alumni Association Board of Directors membership. In particular, she advised the Travel/Study Program, Stanford Sierra Camp, and STANFORD magazine. She also was a key contributor to the J.E. Wallace Sterling Awards for student volunteer service. A tireless fundraiser, Fran also worked closely with the Office of Development as a member of the Major Gifts Committee. Her influence remains present in Stanford's leadership through her involvement in the nomination of alumni to the university's Board of Trustees. Fran was a leader in her community, serving on the boards of several Bay Area organizations, including Menlo School, Castilleja School, the Palo Alto Medical Foundation, the Peninsula Center for the Blind and Visually Impaired, the YMCA, the Peninsula Bridge Program, Family Service Mid-Peninsula, Community Foundation Silicon Valley and Avenidas (senior services for the mid-Peninsula). We hope that the pleasure she took in her work on behalf of Stanford and other organizations will inspire all Stanford alumni. The Stanford Alumni Association and Stanford University would like to thank all the donors for their generous contributions. The Frances C. Arrillaga Alumni Center was erected in 2000 and almost entirely funded by the generosity of Stanford alumni who are regular donors to Stanford's core academic needs. Donor recognition plaques throughout the building recognize the generosity of donors to the Frances C. Arrillaga Alumni Center.	common_crawl_stanford.edu_421
We’re pleased to offer Stanford Alumni Association members savings up to 35% off* single-day ski lift tickets (purchased online) at Palisades Tahoe or Alpine Meadows resorts. Discounts for the 2024-25 ski season are now live. Palisades Tahoe Discount Loading... Benefit Details Up to 35% off* single-day ski lift tickets (purchased online) at Palisades Tahoe or Alpine Meadows resorts. Tickets are date specific and dynamically priced, so act early for the best possible deals. Some blackout dates apply. Purchase Online Once logged in to your SAA membership account, copy the provided promo code shown above and include it in the "apply promo code" box during check out. *Tickets are date specific and are only valid on the date(s) to which they are assigned for the 2024-25 ski season. Please check with Palisades Tahoe for projected open and close dates. Unused lift tickets expire on the last day of the 2024–25 season. All sales are final. No exchanges. No refunds. Expired products have no value. This program is a privilege reserved for SAA members and may not be distributed. Violation of the guidelines will result in the deactivation of the URL and termination of this program. Please note: When terrain and ski lifts are limited, ticket sales may be limited. Securing your ticket online and ahead of time through your SAA link is the best way to reserve your spot on the mountain! Tickets are subject to availability and must be ordered online in advance. About Palisades Tahoe Voted North America's best ski resort for three years in a row and host of the 1960 Winter Olympics, the Palisades Tahoe legacy has maintained a vibrant ski culture and mountain energy that spans from village to peak. Its spirit of outdoor adventure is reflected by the views of Lake Tahoe’s translucent waters, and its inspiration is carved into the slopes of the Sierra mountains. With consistent snowfall that stays around well into the spring, Palisades Tahoe boasts one of the longest ski and snowboard seasons in the country. Visit your favorite Lake Tahoe ski resort today! Palisades Tahoe 1960 Squaw Valley Rd Olympic Valley, CA 96146 (800) 403-0206 About Alpine Meadows Tucked between Truckee and Tahoe City, Alpine Meadows Resort is a picturesque playground for families and off-the-radar thrill-seekers alike. Chalet-style lodges and approachable hospitality mean that the skiing and riding experience always comes first. From easy-riding progression parks to wide-open bowls, Alpine Meadows brings an exciting challenge to any level of skier or snowboarder. And with more than 100 trails spread over 2,400 acres, mountain adventures (and groomed runs) await at every bump, jump, and chairlift. Visit one of Lake Tahoe's hidden gem ski resorts today! Alpine Meadows Ski Resort 2600 Alpine Meadows Rd Alpine Meadows, CA 96146 (800) 403-0206	common_crawl_stanford.edu_422
Benefit Details 10% discount on a single ticket (and up to one guest)* for most Stanford Live performances. Bing Concert Hall Ticket Office 327 Lasuen Street (at Museum Way) Stanford University, Stanford, CA 94305 Open from Tuesday to Friday from 12:00–5:00 p.m. and 1.5 hours before curtain time on performance days. Purchase In-Person Visit the Bing Concert Hall Ticket Office and inform the sales associate that you are an SAA member. You'll be asked to show your Stanford Alumni Association membership card to receive discounted tickets. Purchase by Phone Call (650) 724-2464 and inform the sales associate that you are an SAA member. You'll be asked to provide your SAA member number found on your Stanford Alumni Association membership card. When picking up your tickets at Bing Concert Hall Will-Call (no tickets will be mailed), you'll be required to show your SAA membership card. Purchase Online Once logged in to your SAA membership account, visit the custom website above to receive access to member pricing. When picking up your tickets at Bing Concert Hall Will-Call (no tickets will be mailed), you'll be required to show your SAA membership card. *SAA discount cannot be combined with other discounts (e.g. faculty/staff discount, subscription discount). SAA membership will be verified after a purchase has been completed.	common_crawl_stanford.edu_423
How to Talk to Strangers (external link)from STANFORD magazine Meeting new people can be uncomfortable. You should do it anyway. And Stanford alumni want to help you get there. Whether you’re an alum or a student, find opportunities to network, get career advice, and lend a hand. A classmate from freshman year, a contact in an industry, a sounding board for career advice—find them all in the Alumni Directory, the only verified online listing of Stanford alumni. Update your profile to make better connections. Fellow alums are hiring, and you might be the perfect fit. Explore alumni-posted jobs on the official LinkedIn Group only for verified Stanford alums. Whether you’re focused on professional goals or personal growth, the support of a community of alumni who share your interests can go a long way. Not sure how to navigate this new chapter? You don’t need to go it alone. If you completed your undergrad or graduate degree in the past five years, there are career development resources just for you. Regardless of where you are on your career path, Stanford is here to help connect you to the right resources. Need a career coach to help you through a transition? If you graduated within the past five years, contact a Stanford Career Education career coach through Stanford PlusFive. If it’s been more than five years since you graduated, view bios of career coaches you can contact for a complimentary first session. from STANFORD magazine Meeting new people can be uncomfortable. You should do it anyway. Embarking on a new path. Considering your next professional step. Switching to a different industry. If you’ve ever found yourself at one of these career crossroads, you could have benefited from a career conversation. Thinking about grad school? Read tips or contact an advisor to help you create a path to graduate or professional school that fits your needs and interests. Get up to speed on the dos and don’ts of connecting with fellow alums with these short videos. Have advice to offer students or alums? Join fellow alums in posting job openings and prospects on the Stanford Alumni LinkedIn group. Connect with up-and-coming Stanford talent by posting jobs and internships on Handshake. Raise your hand to mentor students or recent grads, or find your own mentor in an alumni volunteer who wants to help. Sound easy? Introduce yourself with your name, photo, location, headline, and professional experience, and offer career support to help students and recent grads. Ready to network? Looking to learn? Find events online or near you that take your career goals to the next level.	common_crawl_stanford.edu_424
Build Your Network Through Career Conversations: What to Do Before, During and After Embarking on a new path. Considering your next professional step. Switching to a different industry. If you’ve ever found yourself at one of these career crossroads, you could have benefited from a career conversation. Also known as an informational interview, a career conversation is a chance for you to learn from someone knowledgeable about your area of interest. From professional guidance to industry information and more, there’s so much you could gain over a cup of coffee (or Zoom call!) with the right person. Career conversations are also a natural way to build new connections. Your Stanford alumni family includes professionals around the world in a variety of roles, companies and industries—and these conversations can help you tap into that network in a meaningful way. Our advice? Make time to chat with a few alumni with different experiences, and focus on building genuine relationships. You’ll be glad you did. Keep reading below for the inside scoop on what you need to prepare for before, during and after the interview. Need help making an informational interview request to an alum? Check out our tips for cold contacting alumni. 7 Steps to Ensuring an Effective Career Conversation Before the conversation Prepare, prepare, prepare. Think about why you contacted this person and what you’d like to learn from them, creating a list of five to seven questions you want to ask. Then prioritize your questions by order of importance, so you can cover the most pressing ones first in case your meeting is cut short. Next, research the person, role, company and industry. You can find information on LinkedIn, visit the organization’s website, or even create a Google alert to receive email notifications about specific keywords. Bonus: Look up the alum in the Stanford Alumni Directory to find commonalities that may help facilitate your connection (like major, student activities, residences and more). It never hurts to have a few topics in your pocket! Lastly, don’t forget to develop your elevator pitch and prepare an answer to the question “How can I help?” During the conversation Introduce yourself It’s a good idea to start the interview with a quick refresher of who you are and what you hope to learn. (Need a starting point? Scroll down to the “Sample Introduction” below.) Now’s also the time to put that elevator pitch to use and give the alum a sense of your experience, skills, interests or values. Aim for it to be about 90 seconds long. Ask thoughtful questions People enjoy talking about themselves and sharing what they know, so try asking about the person’s work, role or company, or seek advice and perspective on their field. (Scroll down to the “Sample Questions” below for ideas.) Just remember that since you requested the meeting, it's up to you to keep the conversation going and be mindful of the time. Ask for introductions or contacts One of the best ways to build your network is to ask for an introduction or a contact—and often, all you need to do is ask. Say “thank you” Always, always, always say thank you. Ask if you can stay in touch (e.g., by connecting on LinkedIn or exchanging contact info) if you have additional questions or updates. After the conversation Follow up After the meeting, send an email to thank them for their time, listing specific ways they’ve been helpful to you. (See examples in the “Sample Emails” section below.) Think about ways you might stay in touch. Whether you share job progress or send articles they might find interesting, you have options for keeping the conversation going—and don’t forget to think about how you might help them in the future. Reflect and plan your next steps Take a few moments to jot down what you’ve learned, whether it was new information about an industry, helpful career advice or ideas about what to do next. Career Conversations: Sample Materials to Get You Started Sample Introduction “I really appreciate your taking the time to talk with me. I graduated from Stanford in (year) with a degree in (major). I’m especially interested in learning more about (role, organization, field) as I explore various career opportunities in (industry). I’d appreciate any insights or advice you can offer me about ________ (career path, role, company or industry).” Sample Questions Role/career path Can you describe the work you do in a typical week? What do you enjoy most about this role? What challenges are associated with this position? What is the career trajectory for someone in this position? What skills and/or experience are necessary? Is there anything you would change about your career/education now that you are in your current role? What lifestyle choices have you made in your industry? Is there anything about the field/organization/role you wished you’d known before entering it? As a (insert identity/background), how did you navigate your career path and find workplace allies? Team If you think of a successful employee currently in your department/organization, what makes them stand out? What skills make them successful? What is the balance between teamwork and individual work in this position? What is the biggest challenge facing the department/organization right now? What kinds of backgrounds do people in this organization (field/team) have? How are new employees onboarded into the company/department? What are typical work schedules for employees? Is it common to work nights/weekends? What does belonging and inclusion look like on the team and what strategies help cultivate that? Have you found helpful ways to hold colleagues accountable to diversity and inclusion goals on your team or projects? Organization/industry How would you describe the work environment or company/department culture? How has the COVID-19 pandemic changed the way work is done in your organization/field? What will be the likely changes in this industry in the next few years? What strategies do people use to stay up to date on trends in the industry? What are typical career paths in this field? How does this organization define diversity? What lenses of diversity has the organization made a direct commitment toward? How has your organization put into action your DEI priorities (e.g., hired a chief diversity officer, actively support diverse suppliers/contractors, offer employee DEI training, annual compensation equity analysis)? Does your company have any employee resources or affinity groups that help to cultivate a sense of belonging? What other types of organizations do (name of role) work in? How do you see the next few years in terms of job prospects in this field? What are the current needs in the field? What changes are expected? What have diversity and inclusion policies entailed in this organization/industry? How important is diversity to this organization? Where can I learn more about the diversity initiatives? Do you find that leadership teams are generally diverse throughout the industry? Are you aware of any internal (or external) programs in place to support diversity Research (Higher Education) Can you tell me more about the research being done? What kinds of questions is the research trying to answer? My experience is in X and interests are X, Y and Z. Would someone with my background be an asset in your lab? What are the weekly commitments away from the bench (lab meetings, journal clubs, etc.)? What expertise/skill set is most important among the lab team Work values How does the organization support ongoing learning and training for employees? What opportunities are there for advancement? Which values of the company do you see play out on a daily basis? Has the organization made any formal commitments in support of racial equity? Is it possible to balance career and personal life well here? Advice/insight What advice would you give me as I start my career in (industry)? Where do the people who have had these roles go next? Given my background and interests, are there other organizations you might suggest I explore? How would you advise me to get started on building experience in this field? Are there certain classes or training programs you would recommend for building experience for this type of position? Can you provide any resources (identity or industry specific) that were helpful to you in this stage of your career? Is there anything about the field/organization/role you wished you’d known before entering it? Hiring practices Does the company hire throughout the year or are there specific recruiting periods? I understand you currently don’t have openings in the department/organization. How do you recommend I stay in touch to learn about future openings? What do hiring managers look for in a résumé and cover letter? What is the application process like? What is the interview process like? What kinds of questions are typically asked? What is negotiable in this position? What is a typical salary range for this role? Is the salary negotiable? What, if anything, did you negotiate? What do you wish you would have negotiated? Next steps If I have more questions, may I contact you? Would you prefer email or phone? I would love to keep in touch, could I add you as a connection on LinkedIn? You've been very generous with your time, and you've given me several new ideas to explore. Do you know anyone in _____________ (field) or at __________ (company) who might be willing to talk with me? Sample Thank-You Emails Be sure to send an email within 24 to 48 hours after meeting with an alum to express your appreciation and let them know how the meeting helped you. Sample Email #1 Subject: Your Name - Thank You Dear (Name of Alum), It was a pleasure speaking with you earlier today. I truly appreciate that you took time out of your schedule to share your story with me and offer valuable insight about entering the (name of field/industry). I will certainly follow your suggestion to (advice shared with you). Thank you again for your time and for all the information. Best, Your Name Email / Phone Sample Email #2 Subject: Thank you so much! Hello (Name of Alum), It was so nice to meet with you today! Thank you for taking the time to answer my (many) questions and to discuss what it’s like working at (Company). It seems like an amazing place to work. I especially loved hearing about [something you enjoyed talking about]. I look forward to staying in touch as I continue (my job search/figuring out my next step). I definitely plan on using your advice to (piece of advice shared with you). Thank you again! All the best, Your Name Phone / Email Sample Email #3 Subject: Thank you Dear (Name of Alum), Thank you for taking the time to speak with me today. Your insights were very helpful and have confirmed my decision to gain additional work experience in the field before applying to graduate school. I will continually check the two websites you suggested for job leads, and have already contacted the ABC professional association regarding membership. I’ll be sure to follow up in the near future to let you know about my progress. Thank you again for all your help. Sincerely, Your Name Email / Phone	common_crawl_stanford.edu_425
Cold Contacting an Alum? Here’s What You Should Do. No, it’s not just you: networking can feel pretty awkward at times. And chances are, most alumni might even agree with you—so if they find themselves on the receiving end of an out-of-the-blue message from you, they’ll generally respond positively as long as it’s a well-framed request. But before you try to Ernest Hemingway the perfect message, there are a few steps you should follow first. For starters, do some research to find an alum whose background you find interesting. Next, discover common points of interest by looking at their profile in the Alumni Directory and on LinkedIn. And finally, think about what questions you’d like to ask. Once you’ve gathered all this information, you’re ready to draft your message. What to include in your cold contact message to an alum Who: Start things off with a brief intro about yourself. After all, this is a chance for the alum to get to know you, too. How: Mention how you found the alum and their contact information (LinkedIn? Alumni Directory? Mutual acquaintance?). Why: Explain why you’re interested in speaking with them, including what you have in common—like Stanford student group, major or industry—and what you’d like to learn from them. What’s next: Ask if they’re available for a 20-minute chat to discuss their work and/or career path in the next couple of weeks. Thanks: Of course, you’ll want to wrap up your message by thanking the alum for considering your request. What not to include in your cold contact message to an alum Don’t ask for a job or for funding. Don’t send a LinkedIn request without a message about why you want to connect. Don’t contact alumni for commercial or political reasons. Don’t share their contact information with others without their permission. A few reminders about cold contacting Keep your initial message short and sweet. Be flexible and schedule around the alum’s availability. Be patient. Wait a week for the alum to respond; if you don’t hear back by then, send a follow-up asking if they have 15 minutes to talk—or, if not, if there’s someone they can refer you to for a career conversation. Still no response? It’s time to move on to another contact. Whether it’s your first time cold contacting an alum or whether you’ve lost count by now, prepping for that initial contact is the best way to set yourself up for success. And remember, you’re not in it alone—SAA is here to help you every step of the way. Keep reading below for some sample messages to get you started. Have questions? Contact careerservices@alumni.stanford.edu. Sample Emails and LinkedIn Messages for Cold Contacting Alumni Email Request for Informational Interview (Example #1) Subject: Request for informational interview from [Stanford student/recent Stanford graduate] Dear (Name of Alum), I was excited to find your profile in the Stanford Alumni Directory. I’m a recent graduate in human biology at Stanford and am currently exploring careers in global health. I’d like to learn more about your experience as a program manager with Save the Children. Would you be willing to meet sometime in the next few weeks for 20–30 minutes (on the phone or over Zoom) to discuss your current role at Save the Children and how you prepared for a career in global health? Thanks so much for considering—I look forward to hearing from you! Sincerely, Your Name Email Request for Informational Interview (Example #2) Subject: Informational interview request about [XYZ point of interest] Dear (Name of Alum), I recently graduated from Stanford with a B.A. in international relations. I saw your profile on LinkedIn and found your email in the Stanford Alumni Directory. From your LinkedIn profile, I see that you’ve worked at a variety of NGOs, most recently in Syria. As I’ve focused my own studies on the Middle East, including time studying abroad in Cairo, I’d like to return to the region to work full time. I’d love to hear about your experiences living and working in the region, as well as any advice you might have for me as I begin an international job search. Are you available for a quick chat over phone or Zoom sometime within the next few weeks? Your insight would be extremely valuable. Thanks so much for considering this request. Sincerely, Your Name LinkedIn Request for Informational Interview Dear (Name of Alum), I recently graduated from Stanford with a degree in psychology. I was excited to find your profile on LinkedIn as I’m interested in pursuing a career with (the State Department/on Capitol Hill/ with an international nonprofit focused on human rights). Would you be willing to speak with me for 20–30 minutes about your career path or your experience working at ABC Organization? Thank you for considering this request. Your Name	common_crawl_stanford.edu_426
Skip to main content Alumni Directory (external link) Email (external link) Search Events Featured Events Reunions Event Space Reading & Resources News & Stories Learn STANFORD Magazine (external link) Career Connections Newsletters Racial Justice Programs & Perks Alumni Perks Alumni Membership SAA Membership Card (external link) Sierra Programs Travel/Study Wine Collection (external link) Alumni Store (external link) Communities Clubs & Groups Young Alumni Students Volunteer Volunteer Opportunities Beyond the Farm Stanford Associates Trustee Nominations About About SAA Alumni Center Contact Us Alumni Directory (external link) Email (external link) Search Events Featured Events Reunions Event Space Reading & Resources News & Stories Learn STANFORD Magazine (external link) Career Connections Newsletters Racial Justice Programs & Perks Alumni Perks Alumni Membership SAA Membership Card (external link) Sierra Programs Travel/Study Wine Collection (external link) Alumni Store (external link) Communities Clubs & Groups Young Alumni Students Volunteer Volunteer Opportunities Beyond the Farm Stanford Associates Trustee Nominations About About SAA Alumni Center Contact Us Events Featured Events Reunions Event Space Reading & Resources News & Stories Learn STANFORD Magazine (external link) Career Connections Newsletters Racial Justice Programs & Perks Alumni Perks Alumni Membership SAA Membership Card (external link) Sierra Programs Travel/Study Wine Collection (external link) Alumni Store (external link) Communities Clubs & Groups Young Alumni Students Volunteer Volunteer Opportunities Beyond the Farm Stanford Associates Trustee Nominations About About SAA Alumni Center Contact Us Section menu General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass Alumni Directory FAQs Skip past sidebar to page content Section menu General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass Collapse all Expand all What is the Alumni Directory? + Why should I keep my profile updated? + What is Stanford Pass? + How should I go about contacting someone? + Who or what can I find in the Alumni Directory? + How do I find what I need? + What if my contact has changed their name? + How do I find people offering career support? + Who has access to the directory? + How do I adjust what people can see on my profile? +	common_crawl_stanford.edu_427
Networking Tips for Students and Alumni Are you a student or recent graduate? Check out these three easy steps for a great conversation with alumni: Before: Brainstorm and rank 3–4 thoughtful questions to ask an alum about their Stanford experience, career experience, or perspective. Examples include: What is one thing I should definitely do at Stanford before I graduate? Tell me about your post-Stanford path. Have there been any surprises along the way? What advice would you give someone just starting out in your field? Develop a brief (30–60 seconds) pitch of who you are: this can include your strengths, skills, and what you’re most curious about. During: Introduce yourself and share your pitch. Ask questions that get the alum to share reflections about their time at Stanford, career path, or post-Stanford life. At the end of the conversation, thank them for the opportunity to speak with them, tell the alum what you found helpful in your conversation, and ask if you can connect with them over email or LinkedIn. Share your LinkedIn QR code. After: Send the alum a short email or LinkedIn message letting them know you appreciate them taking the time to speak with you, what you found helpful in your conversation, and that you hope you can stay in touch. Are you an alum? Check out these three easy steps for a great conversation with students or recent graduates: Before: Brainstorm and rank 3–4 thoughtful questions to ask the student or recent graduate. Reflect on your time as a student and consider what Stanford experiences you might share with them. Stanford life: What has been your favorite class, event, or pastime at Stanford? Can you share any causes or issues you are curious or care deeply about solving? Is there anything specific you hope to accomplish in the first few years after graduating Stanford? During: Introduce yourself and include a personal fact that can’t be found on LinkedIn. Ask questions that get the student or recent grad to share reflections about their time at Stanford or their post-Stanford aspirations. At the end of the conversation with the student or recent grad, thank them for the opportunity to speak with them, tell them what you learned from the conversation, and offer to connect via email or share your LinkedIn QR code. After: Respond when students or recent grads reach out, and be clear about your availability and how you can help. Be open to making referrals and continuing the connection.	common_crawl_stanford.edu_428
Make the Most of an Internship (external link)from STANFORD magazine 5 essentials from industry experts. Thinking about your next professional step? Get closer to your goals by making connections you can carry, even when you’re off the Farm. The Alumni Directory, Stanford Alumni Groups, alumni events—access them all with your Stanford Pass login. Be sure yours is set up (it’s different from your SUNet ID!) so you can tap into more programming and resources that go beyond student life. Did you know that the Alumni Directory with 240,000 alums is the only verified online listing of Stanford alumni and is available to students too? Set up your profile to start connecting with alumni who are in your field or where you want to be. You control what info you display, and with these tips for cold contacting, you’ll be set up to reach out. Friends in your city. Peers in your industry. A crew with a shared culture. Who will you discover? Visit Axess to opt in to Stanford Alumni Groups and start expanding your network. (Tip: Slide the bar to “Y” to opt in. You’ll receive a confirmation email with login instructions within 24 hours.) Yup—the alumni LinkedIn group is for students, too! Use your alumni.stanford.edu email address to join this private group, where other verified Stanford students and alums can view and post jobs (#hiring), share updates, and message one another. Want the wisdom of someone who’s been in your shoes? Get access to over 10,000 alumni volunteers who want to help by creating a Stanford Alumni Mentoring (SAM) account. Then when you’re ready to pursue new opportunities, search for open positions on Handshake, the platform for alumni-posted jobs and internships. from STANFORD magazine 5 essentials from industry experts. from STANFORD magazine Meeting new people can be uncomfortable. You should do it anyway. Embarking on a new path. Considering your next professional step. Switching to a different industry. If you’ve ever found yourself at one of these career crossroads, you could have benefited from a career conversation.	common_crawl_stanford.edu_429
Stanford Alumni Email Service Policy Stanford University currently offers an email account to all alumni who register on the alumni website, meet the Stanford Alumni Association definition or school-based definition of “alum” (by completing three or more quarters in a Stanford degree-granting program or by the completion of the GSB Stanford Executive Program) and enable the service, either at the time of alumni site registration or at a later time by enabling the service in their profile on the alumni site. The account will remain accessible to the alum as long as the alum complies with the Terms of Use which may be changed or amended from time-to-time. Stanford reserves the right to discontinue an alum's email access if the alum uses the account in a way that, in the sole discretion of Stanford, it deems inappropriate as defined in the Code of Conduct. Notwithstanding the above, Stanford reserves the right to terminate the webmail service at any time at its discretion. Stanford does not view or access any content from your Alumni Email account unless explicitly requested to do so by the account owner to provide technical assistance, or in response to an automatic account suspension by Google, or in response to a compulsory legal process mandate. Alumni Email, powered by Google, will provide strong security and privacy protections. Google has provided contractual guarantees that allow users the ability to use the services with the appropriate privacy protections. Google is contractually obligated to protect your confidential information and not to release it to other parties, absent compulsory legal process. You can view more about their specific privacy protections here: https://policies.google.com/privacy In order to provide essential core features for Stanford Alumni Email, Google runs completely automated scanning and indexing processes to offer spam filtering, anti-virus protection, and malware detection. Their systems also scan content to make sure apps work better for users, enabling functionality like search in Gmail or Google Docs. This is completely automated and involves no humans.	common_crawl_stanford.edu_430
Clubs and Groups Discover alumni communities by affiliations that are relevant to you with local and virtual offerings. Meet Your Stanford Community Here you’ll connect with other alumni; professional groups for networking, advice, and opportunities; and shared interest groups hosting a space for discussion, support, and celebration. Interest Groups Hobbies. Community service. Student activities. Even the classifieds. Whatever your focus, there’s likely a group of alums who share your same interest. Professional Groups Connect with others in your industry, get advice from alums or build your network through a professional group. Regional Groups Looking to meet Stanford alumni in your area? Find a regional group to hear first about events in your neighborhood. Shared Identity Groups Find communities that share your identity, culture, race, ethnicity, and offerings for young alumni. Explore Alumni Groups Stanford Alumni Groups is the place to find Alumni volunteer-led communities and programs worldwide. Discover alumni communities and start a conversation or look out for upcoming events near where you live or with communities important to you. Communities Communities range in size and topic from regional and professional groups to shared interest identity, class cohorts, and other interest groups—you can stay connected with the Stanford experience. Events Faculty speaker events, community service, social gatherings, or professional networking, you’ll find in-person and online opportunities to learn and have fun with fellow alumni. Conversations Ask a group for recommendations when moving to a new city, organize an AMA (ask me anything) in a group discussion board, or reach out to another alum in a private direct message. Stanford Alumni Groups offers you a place to connect in real-time or asynchronously with your alumni community. Club Leader Resources Need help managing membership or events, running reports, reaching out, or all of the above? We’ve got you covered with the resources you need to grow your club.	common_crawl_stanford.edu_431
Regional Clubs Clubs and GroupsRegional Clubs - Stanford Club of Anchorage(external link) - Hawaii Stanford Chapter (Oahu Island)(external link) - Stanford Association of Oregon (Greater Portland Region)(external link) - Stanford Alumni in Central Oregon(external link) - Stanford Club of Washington (Seattle and West Washington)(external link) - Stanford Black Alumni Association - Seattle(external link) Northern California Bay Area - South Bay Stanford Alumni(external link) - Stanford Club of San Francisco(external link) - Stanford Club of Marin(external link) - Stanford Young Alumni - Bay Area(external link) - Stanford Professional Women - Bay Area(external link) - Stanford Women's Club of the East Bay(external link) - Stanford Peninsula Alumni(external link) - Stanford Women's Club of San Francisco(external link) - Stanford Bay Area Hiking Club(external link) - Stanford Black Alumni Association - Northern California(external link) - Stanford Club of Rossmoor(external link) - Stanford Asian Pacific American Alumni Club(external link) - Stanford Angels & Entrepreneurs (Entrepreneurship Ventures or Angel Investing)(external link) - Stanford Entrepreneurs (Business Entrepreneurship in the Bay Area)(external link) - Stanford Singles Club - Bay Area (Ages 50+ Around the Peninsula and South Bay)(external link) - Stanford Pride(external link) - Stanford Latino Alumni Association of Northern California(external link) - Stanford Club of the East Bay(external link) Central California Greater Los Angeles Area - Stanford Club of Pasadena(external link) - Stanford Club of West Los Angeles(external link) - Stanford Club of Palos Verdes/South Bay(external link) - Stanford Young Alumni - LA(external link) - Stanford Professional Women of Los Angeles County(external link) - Stanford Black Alumni Association - Southern California(external link) - Stanford Latino Alumni Association of Southern California(external link) - Stanford in Entertainment - LA (For Alumni in Entertainment)(external link) - Stanford Angels & Entrepreneurs of Southern California(external link) - Stanford Outdoor Club of LA (Hiking in the Greater LA Area)(external link) - Stanford Ideas & Connections Network (LA Network for Alumni in Consulting)(external link) - Reading to Kids Los Angeles (Community Service Organization in the Greater LA Area)(external link) - Stanford Pride(external link) - Stanford Conejo Salon (Social Group for Alumni in the Conejo Valley)(external link) - West LA Book Club(external link) - Pasadena Book Group(external link) - Stanford South Bay/Long Beach Book Club(external link) - San Fernando Valley Book Club(external link) - Stanford Conejo Valley Book Club(external link) - Stanford Club of the Desert (Greater Palm Springs Area)(external link) Orange County - Phoenix Stanford Chapter(external link) - Stanford Club of Southern Arizona/Tucson(external link) - Stanford Club of Dallas/Fort Worth(external link) - Stanford Club of Houston(external link) - El Paso del Norte Stanford Alumni(external link) - Stanford Club of Austin(external link) - Stanford Club of San Antonio(external link) - Stanford Black Alumni Association - Houston(external link) - Stanford Club of South Florida (Greater Miami Region)(external link) - Stanford Club of Georgia (Greater Atlanta Area)(external link) - Stanford Black Alumni Association - Atlanta(external link) - Stanford Club of Charlotte(external link) - North Carolina Stanford Alumni Club of Raleigh-Durham-Chapel Hill(external link) - Nashville Stanford Club(external link) - Stanford Club of Central Virginia(external link) - Stanford Club of Hampton Roads(external link) Massachusetts New York - Food and Wine Group NYC(external link) - New York Community Service(external link) - Stanford Alumni Startups New York(external link) - Stanford in Arts & Entertainment - New York(external link) - Stanford Black Alumni Association - New York(external link) - Stanford Latino Alumni Association - New York(external link) - Stanford New York Alumni (General Alumni Group for NY Metro Region)(external link) - Stanford Runners of New York(external link) - Stanford Women's Network - New York(external link) - Stanford Young Alumni - New York(external link) Pennsylvania - Stanford Club of Shanghai(external link) - Stanford Club of India (Bangalore, Chennai, Delhi & Mumbai)(external link) - Stanford Club of Thailand (Bangkok)(external link) - Stanford Club of Beijing(external link) - Stanford Club of Hong Kong(external link) - Stanford Club of Pakistan(external link) - Stanford Club of the Philippines(external link) - Stanford Club of Singapore(external link) - Stanford Alumni in South China(external link) - Stanford Club of Southwest China(external link) - Stanford Club of Taiwan(external link) - Japan Stanford Association(external link) - Kansai Stanford Club(external link) - Stanford Club of Korea(external link) - Stanford Angels & Entrepreneurs - India(external link) - German Stanford Association(external link) - Stanford Alumni Association Cyprus(external link) - Stanford Club of Austria(external link) - Stanford Club of Belgium(external link) - Stanford Club of Bulgaria(external link) - Stanford Club of France(external link) - Stanford Club of Great Britain(external link) - Stanford Club of Greece(external link) - Stanford Club Italia(external link) - Stanford Club of Luxembourg(external link) - Stanford Club of the Netherlands(external link) - Stanford Club of Norway(external link) - Stanford Club of Poland(external link) - Stanford Club of Spain(external link) - Stanford Club of Switzerland(external link) - Stanford Angels of the United Kingdom(external link) - Stanford Women's Network - UK(external link) - Beutelsbach XI(external link) Stanford Alumni (650) 723-2021 (800) 786-2586 (toll-free) contact-saa@alumni.stanford.edu Frances C. Arrillaga Alumni Center 326 Galvez Street Stanford, CA 94305-6105	common_crawl_stanford.edu_432
Skip to main content Alumni Directory (external link) Email (external link) Search Events Featured Events All Alumni Events Alumni Community Events (external link) Career Events Student Events University Events (external link) Stanford Athletics Tickets (external link) Reunion Homecoming Reunion 2025 Ways to Volunteer View Schedule Registration Alumni Center Events Host with SAA Event Spaces Planning Resources Los Angeles County Fires Alumni resources & support Reading & Resources News & Stories Recent Stories STANFORD magazine (external link) Newsletters Read Class Notes (external link) Learn Learning Opportunities Recorded Live Talks (external link) Recommended Podcasts Subscribe to Learn List Career Connections Getting Started Students Career Resources Give Career Support Post or Find Jobs (external link) Post or Find Internships (external link) Connect on Alumni Directory (external link) Alumni Linkedin Group (external link) Los Angeles County Fires Alumni resources & support Programs & Perks Perks & Membership Perks for All Alumni Become an SAA Member SAA Membership Card SAA Member Benefits Sierra Programs Summer Family Camp Faculty/Staff Weekend Healthy Living Retreat for Women Memorial Day Weekend Sierra Camp Conference Center (external link) Sierra Programs Updates Travel/Study The Stanford Advantage Find a Destination Experiences Custom Journeys Trip Alerts Shop Stanford Alumni Fan Shop (external link) Stanford Alumni Coffee (external link) Wine Collection (external link) Communities Clubs & Groups Discover Alumni Groups Interest & Affinity Groups Professional Groups Regional Groups (U.S.) Regional Groups (International) School Groups (external link) Shared Identity Groups Leader Resources Young Alumni Recent Grad Resources Stanford 10 Undergraduate Students All Undergraduates Frosh Sophomores Juniors Seniors Senior Awards Senior Commencement Events Graduate Students All Graduate Students Grad Student Awards Grad Commencement Volunteer Volunteer Opportunities Leadership Richard W. Lyman Award Multicultural Alumni Hall of Fame Beyond the Farm Join a Project Project Leaders Stanford Associates Grants Awards Associates Designees Board of Governors Trustee Nominations Board Facts Becoming a Trustee Current Trustees (external link) About About Us Get to Know SAA Departments Executive Leadership Jobs (external link) Alumni Center Amenities & Space Dedication & Donors Contact Us Get in Touch Frequently Asked Questions Update Your Contact Info (external link) Submit a Class Note (external link) Submit an Obituary (external link) Los Angeles County Fires Alumni resources & support Alumni Directory (external link) Email (external link) Search Events Featured Events All Alumni Events Alumni Community Events (external link) Career Events Student Events University Events (external link) Stanford Athletics Tickets (external link) Reunion Homecoming Reunion 2025 Ways to Volunteer View Schedule Registration Alumni Center Events Host with SAA Event Spaces Planning Resources Los Angeles County Fires Alumni resources & support Reading & Resources News & Stories Recent Stories STANFORD magazine (external link) Newsletters Read Class Notes (external link) Learn Learning Opportunities Recorded Live Talks (external link) Recommended Podcasts Subscribe to Learn List Career Connections Getting Started Students Career Resources Give Career Support Post or Find Jobs (external link) Post or Find Internships (external link) Connect on Alumni Directory (external link) Alumni Linkedin Group (external link) Los Angeles County Fires Alumni resources & support Programs & Perks Perks & Membership Perks for All Alumni Become an SAA Member SAA Membership Card SAA Member Benefits Sierra Programs Summer Family Camp Faculty/Staff Weekend Healthy Living Retreat for Women Memorial Day Weekend Sierra Camp Conference Center (external link) Sierra Programs Updates Travel/Study The Stanford Advantage Find a Destination Experiences Custom Journeys Trip Alerts Shop Stanford Alumni Fan Shop (external link) Stanford Alumni Coffee (external link) Wine Collection (external link) Communities Clubs & Groups Discover Alumni Groups Interest & Affinity Groups Professional Groups Regional Groups (U.S.) Regional Groups (International) School Groups (external link) Shared Identity Groups Leader Resources Young Alumni Recent Grad Resources Stanford 10 Undergraduate Students All Undergraduates Frosh Sophomores Juniors Seniors Senior Awards Senior Commencement Events Graduate Students All Graduate Students Grad Student Awards Grad Commencement Volunteer Volunteer Opportunities Leadership Richard W. Lyman Award Multicultural Alumni Hall of Fame Beyond the Farm Join a Project Project Leaders Stanford Associates Grants Awards Associates Designees Board of Governors Trustee Nominations Board Facts Becoming a Trustee Current Trustees (external link) About About Us Get to Know SAA Departments Executive Leadership Jobs (external link) Alumni Center Amenities & Space Dedication & Donors Contact Us Get in Touch Frequently Asked Questions Update Your Contact Info (external link) Submit a Class Note (external link) Submit an Obituary (external link) Los Angeles County Fires Alumni resources & support Events Featured Events All Alumni Events Alumni Community Events (external link) Career Events Student Events University Events (external link) Stanford Athletics Tickets (external link) Reunion Homecoming Reunion 2025 Ways to Volunteer View Schedule Registration Alumni Center Events Host with SAA Event Spaces Planning Resources Los Angeles County Fires Alumni resources & support Reading & Resources News & Stories Recent Stories STANFORD magazine (external link) Newsletters Read Class Notes (external link) Learn Learning Opportunities Recorded Live Talks (external link) Recommended Podcasts Subscribe to Learn List Career Connections Getting Started Students Career Resources Give Career Support Post or Find Jobs (external link) Post or Find Internships (external link) Connect on Alumni Directory (external link) Alumni Linkedin Group (external link) Los Angeles County Fires Alumni resources & support Programs & Perks Perks & Membership Perks for All Alumni Become an SAA Member SAA Membership Card SAA Member Benefits Sierra Programs Summer Family Camp Faculty/Staff Weekend Healthy Living Retreat for Women Memorial Day Weekend Sierra Camp Conference Center (external link) Sierra Programs Updates Travel/Study The Stanford Advantage Find a Destination Experiences Custom Journeys Trip Alerts Shop Stanford Alumni Fan Shop (external link) Stanford Alumni Coffee (external link) Wine Collection (external link) Communities Clubs & Groups Discover Alumni Groups Interest & Affinity Groups Professional Groups Regional Groups (U.S.) Regional Groups (International) School Groups (external link) Shared Identity Groups Leader Resources Young Alumni Recent Grad Resources Stanford 10 Undergraduate Students All Undergraduates Frosh Sophomores Juniors Seniors Senior Awards Senior Commencement Events Graduate Students All Graduate Students Grad Student Awards Grad Commencement Volunteer Volunteer Opportunities Leadership Richard W. Lyman Award Multicultural Alumni Hall of Fame Beyond the Farm Join a Project Project Leaders Stanford Associates Grants Awards Associates Designees Board of Governors Trustee Nominations Board Facts Becoming a Trustee Current Trustees (external link) About About Us Get to Know SAA Departments Executive Leadership Jobs (external link) Alumni Center Amenities & Space Dedication & Donors Contact Us Get in Touch Frequently Asked Questions Update Your Contact Info (external link) Submit a Class Note (external link) Submit an Obituary (external link) Los Angeles County Fires Alumni resources & support Section menu General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass Alumni Email FAQs Skip past sidebar to page content Section menu General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass I Want To Know if I Have an Alumni Email Collapse all Expand all Who is eligible for an alumni email account? + How do I get an alumni email account? + What if I also have a GSB alumni email account? + I Want To Learn How To Access My Email Collapse all Expand all How do I access my alumni email account? + Do I need to log out of Gmail separately when I log out of the Stanford Alumni Association website? + Where do I go to log out of Gmail? + Is there any reason that I would not be able to access my alumni email account? + Does Stanford have access to my alumni email account? + I Need Help With My Email Account Collapse all Expand all Who sets up and administers Stanford Alumni email accounts? + How secure is my alumni email account? + My name has changed. How do I update my alumni email address? + When will Google stop supporting basic authentication for third-party apps or devices that require users to connect to their Google Workspace account with a username and password? + What should I do now that Google no longer supports the third-party app I connect to my Google Workspace account using a username and password? + I Want To Change My Email Settings Collapse all Expand all Can I forward incoming mail to another address? + How do I delete a forwarding setting? + How do I add contacts? + How do I create labels or folders? + How do I create filters? + How do I create an alias? + How do I create a signature? + How do I change the time zone? + How do I add a vacation response? + How do I enable an Outlook-style preview pane? + I have an @stanfordalumni.org email address. Why does the email address in the right corner of my mail screen say username@alumni.stanford.edu? + Why can't I use my @stanfordalumni.org email address to log in to other Google products? + I Want To Learn Why My Alumni Email Is Different Collapse all Expand all How does my personal Gmail account relate to my alumni email account? + What happens to my Alumni email account if my total storage usage exceeds the limit when the 15 GB limit is instituted? + Can I merge an existing Google account with my alumni email account? + Can I keep both my personal Google account and my alumni email account open in the same browser? + What applications are being offered with my Stanford Alumni email account? + Still need help with email? Get in touch. (external link) Need a Stanford Pass account?	common_crawl_stanford.edu_433
Skip to main content Alumni Directory (external link) Email (external link) Search Events Featured Events All Alumni Events Alumni Community Events (external link) Career Events Student Events University Events (external link) Stanford Athletics Tickets (external link) Reunion Homecoming Reunion 2025 Ways to Volunteer View Schedule Registration Alumni Center Events Host with SAA Event Spaces Planning Resources Los Angeles County Fires Alumni resources & support Reading & Resources News & Stories Recent Stories STANFORD magazine (external link) Newsletters Read Class Notes (external link) Learn Learning Opportunities Recorded Live Talks (external link) Recommended Podcasts Subscribe to Learn List Career Connections Getting Started Students Career Resources Give Career Support Post or Find Jobs (external link) Post or Find Internships (external link) Connect on Alumni Directory (external link) Alumni Linkedin Group (external link) Los Angeles County Fires Alumni resources & support Programs & Perks Perks & Membership Perks for All Alumni Become an SAA Member SAA Membership Card SAA Member Benefits Sierra Programs Summer Family Camp Faculty/Staff Weekend Healthy Living Retreat for Women Memorial Day Weekend Sierra Camp Conference Center (external link) Sierra Programs Updates Travel/Study The Stanford Advantage Find a Destination Experiences Custom Journeys Trip Alerts Shop Stanford Alumni Fan Shop (external link) Stanford Alumni Coffee (external link) Wine Collection (external link) Communities Clubs & Groups Discover Alumni Groups Interest & Affinity Groups Professional Groups Regional Groups (U.S.) Regional Groups (International) School Groups (external link) Shared Identity Groups Leader Resources Young Alumni Recent Grad Resources Stanford 10 Undergraduate Students All Undergraduates Frosh Sophomores Juniors Seniors Senior Awards Senior Commencement Events Graduate Students All Graduate Students Grad Student Awards Grad Commencement Volunteer Volunteer Opportunities Leadership Richard W. Lyman Award Multicultural Alumni Hall of Fame Beyond the Farm Join a Project Project Leaders Stanford Associates Grants Awards Associates Designees Board of Governors Trustee Nominations Board Facts Becoming a Trustee Current Trustees (external link) About About Us Get to Know SAA Departments Executive Leadership Jobs (external link) Alumni Center Amenities & Space Dedication & Donors Contact Us Get in Touch Frequently Asked Questions Update Your Contact Info (external link) Submit a Class Note (external link) Submit an Obituary (external link) Los Angeles County Fires Alumni resources & support Alumni Directory (external link) Email (external link) Search Events Featured Events All Alumni Events Alumni Community Events (external link) Career Events Student Events University Events (external link) Stanford Athletics Tickets (external link) Reunion Homecoming Reunion 2025 Ways to Volunteer View Schedule Registration Alumni Center Events Host with SAA Event Spaces Planning Resources Los Angeles County Fires Alumni resources & support Reading & Resources News & Stories Recent Stories STANFORD magazine (external link) Newsletters Read Class Notes (external link) Learn Learning Opportunities Recorded Live Talks (external link) Recommended Podcasts Subscribe to Learn List Career Connections Getting Started Students Career Resources Give Career Support Post or Find Jobs (external link) Post or Find Internships (external link) Connect on Alumni Directory (external link) Alumni Linkedin Group (external link) Los Angeles County Fires Alumni resources & support Programs & Perks Perks & Membership Perks for All Alumni Become an SAA Member SAA Membership Card SAA Member Benefits Sierra Programs Summer Family Camp Faculty/Staff Weekend Healthy Living Retreat for Women Memorial Day Weekend Sierra Camp Conference Center (external link) Sierra Programs Updates Travel/Study The Stanford Advantage Find a Destination Experiences Custom Journeys Trip Alerts Shop Stanford Alumni Fan Shop (external link) Stanford Alumni Coffee (external link) Wine Collection (external link) Communities Clubs & Groups Discover Alumni Groups Interest & Affinity Groups Professional Groups Regional Groups (U.S.) Regional Groups (International) School Groups (external link) Shared Identity Groups Leader Resources Young Alumni Recent Grad Resources Stanford 10 Undergraduate Students All Undergraduates Frosh Sophomores Juniors Seniors Senior Awards Senior Commencement Events Graduate Students All Graduate Students Grad Student Awards Grad Commencement Volunteer Volunteer Opportunities Leadership Richard W. Lyman Award Multicultural Alumni Hall of Fame Beyond the Farm Join a Project Project Leaders Stanford Associates Grants Awards Associates Designees Board of Governors Trustee Nominations Board Facts Becoming a Trustee Current Trustees (external link) About About Us Get to Know SAA Departments Executive Leadership Jobs (external link) Alumni Center Amenities & Space Dedication & Donors Contact Us Get in Touch Frequently Asked Questions Update Your Contact Info (external link) Submit a Class Note (external link) Submit an Obituary (external link) Los Angeles County Fires Alumni resources & support Events Featured Events All Alumni Events Alumni Community Events (external link) Career Events Student Events University Events (external link) Stanford Athletics Tickets (external link) Reunion Homecoming Reunion 2025 Ways to Volunteer View Schedule Registration Alumni Center Events Host with SAA Event Spaces Planning Resources Los Angeles County Fires Alumni resources & support Reading & Resources News & Stories Recent Stories STANFORD magazine (external link) Newsletters Read Class Notes (external link) Learn Learning Opportunities Recorded Live Talks (external link) Recommended Podcasts Subscribe to Learn List Career Connections Getting Started Students Career Resources Give Career Support Post or Find Jobs (external link) Post or Find Internships (external link) Connect on Alumni Directory (external link) Alumni Linkedin Group (external link) Los Angeles County Fires Alumni resources & support Programs & Perks Perks & Membership Perks for All Alumni Become an SAA Member SAA Membership Card SAA Member Benefits Sierra Programs Summer Family Camp Faculty/Staff Weekend Healthy Living Retreat for Women Memorial Day Weekend Sierra Camp Conference Center (external link) Sierra Programs Updates Travel/Study The Stanford Advantage Find a Destination Experiences Custom Journeys Trip Alerts Shop Stanford Alumni Fan Shop (external link) Stanford Alumni Coffee (external link) Wine Collection (external link) Communities Clubs & Groups Discover Alumni Groups Interest & Affinity Groups Professional Groups Regional Groups (U.S.) Regional Groups (International) School Groups (external link) Shared Identity Groups Leader Resources Young Alumni Recent Grad Resources Stanford 10 Undergraduate Students All Undergraduates Frosh Sophomores Juniors Seniors Senior Awards Senior Commencement Events Graduate Students All Graduate Students Grad Student Awards Grad Commencement Volunteer Volunteer Opportunities Leadership Richard W. Lyman Award Multicultural Alumni Hall of Fame Beyond the Farm Join a Project Project Leaders Stanford Associates Grants Awards Associates Designees Board of Governors Trustee Nominations Board Facts Becoming a Trustee Current Trustees (external link) About About Us Get to Know SAA Departments Executive Leadership Jobs (external link) Alumni Center Amenities & Space Dedication & Donors Contact Us Get in Touch Frequently Asked Questions Update Your Contact Info (external link) Submit a Class Note (external link) Submit an Obituary (external link) Los Angeles County Fires Alumni resources & support Section menu General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass Stanford Giving FAQs Skip past sidebar to page content Section menu General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass General Questions Alumni Directory Alumni Email Membership Giving My Account Stanford Pass I Want To Learn More About Making a Gift Collapse all Expand all Where can I find information about giving to Stanford? + Who can I contact with questions about giving? + I need help with my gift. + Still need help with Giving? Get in touch. (external link) Ready to make your gift? Start here. (external link)	common_crawl_stanford.edu_434
Stanford Pass Do I Have Stanford Pass? If you have an alumni username and password, you’re all set! Your existing credentials will continue to work as they always have. To ensure you can reset your password if you’ve forgotten it or have been locked out of your account, keep your email address up-to-date in your account. Since you use Stanford Pass to log into your alumni email, we recommend that you do not use your alumni.stanford.edu address as your primary email address on file. You can update your primary and secondary email address in your account. Don’t have a Stanford Pass? You’ve come to the right place—set up your account today by creating a unique username and password. If you know your SUID (the number on your Stanford ID card), include it when you create your account to avoid any delays in accessing alumni services. I Want To Learn About Stanford Pass What Services Use Stanford Pass? Alumni-branded Email Address Let fellow alums and potential employers know you’re a Stanford alum simply by hitting “send.” Get your free @alumni.stanford.edu email address now. Alumni Directory Your alumni network is just a click away. Explore the private Alumni Directory to find professional contacts, friends, classmates and more. My Account Keep your alumni account and Directory profile up to date. You’ll receive more relevant communications by keeping your information current, and you can choose what you want to appear in the Alumni Directory. Stanford Alumni Groups Access alumni-hosted events and over 500 Stanford alumni communities. Students accessing Stanford Alumni Groups must also opt into Stanford Alumni Groups from your privacy settings in Axess and create a Stanford Pass account. Once done, you can browse clubs and groups.	common_crawl_stanford.edu_435
Access to online databases with thousands of publications Read, research, and learn with free access to searchable databases containing thousands of journals and publications. Log in with your SAA credentials to access the databases. While you have some perks and benefits as a cherished alum, our Stanford Alumni Association membership grants you access to various special perks and benefits exclusive to members. Unlock exclusive benefits and upgraded versions of select perks—while also supporting programs for students and alums—with a Stanford Alumni Association (SAA) membership. Read, research, and learn with free access to searchable databases containing thousands of journals and publications. Log in with your SAA credentials to access the databases. Keep learning alive with 15% off any in-person or online Stanford Continuing Studies course. Membership is verified upon receipt of your registration. Enhance your skills. Expand your knowledge. Engage in practical learning. Take a course through Stanford Online and receive a 15% discount on most professional education courses and programs. Stanford GSB Executive Education programs taught by world-renowned Stanford Graduate School of Business faculty. Enjoy low rates, 125% financing, up to 90 days no payments, and an exclusive alumni rate discount. Find low rates and fees on purchase and refinance mortgages with an exclusive alumni fee discount, as well as HELOCs and fast approvals. Savings of up to 35% off single-day lift tickets (purchased online) at Palisades Tahoe. Log in with your SAA credentials to access the discount. Get where you need to go with up to 35% off Avis rental rates. Log in with your SAA credentials to access the discount. Get where you need to go with up to 35% off Budget rental rates. Log in with your SAA credentials to access the discount. Receive discounted hotel rates worldwide through our partnership with HBC Travel Club. Log in with your SAA credentials to access the discount. Explore the world with waived non-member fees ($450/trip) on all Stanford Travel/Study trips. Head to Fallen Leaf Lake with exclusive access to family-friendly activities, retreats, and more at Stanford Sierra Camp. Enjoy access to the private golf course consistently rated as one of the finest in the world. Bring your SAA membership card and another form of ID. Enjoy 10% off select in-store and online purchases at the Stanford Bookstore. Proof of membership is required. Log in with your SAA credentials to access the discount. Make reunion even sweeter with a 10% discount on Reunion Homecoming registration and a special welcome-back gift. Enjoy discounts on alumni-selected wines from high-quality West Coast wineries. Available in participating states. Show your SAA membership card to purchase day or monthly passes at the front desk of Stanford's facilities and enjoy gyms, pools, and other recreational programs. Get 10% off most Stanford Live performance tickets for you and a guest. Valid on online, in-person, and phone orders. Log in with your SAA credentials to access the discount. Enjoy day-of-game discounts on up to four general admission tickets to home matches. Discount available at the ticket booth with your membership card. See campus like never before with free access for you and one guest at the Hoover Tower Observation Platform. During Reunion and Family Weekend, complimentary guest access is not available. Get a 10% discount on membership dues at the leading society for preserving and sharing the University's legacy. Enjoy seven days (14 days for SAA members) of free access per year at Green Library. While a majority of the benefits listed above are available to all members, some are available exclusively to Stanford alums. Undergraduate and graduate alumni of Stanford. Lifetime $695 Lifetime Installment split into five equal annual payments $750 Stanford faculty, staff, interns, residents, fellows, certificate holders, postdocs, retirees, Travel/Study participants and Stanford parents Lifetime $795 Non-alumni, non-affiliate. Benefits are similar to Stanford Affiliate. Lifetime $995 Without the support of our members, the popular programs, services and resources we offer all alumni and students would not be possible.	common_crawl_stanford.edu_436
Gifted Membership FAQs Why is membership gifted to new graduates? Stanford University's long-range vision includes the presidential initiative IDEAL (Inclusion, Diversity, Equity and Access in a Learning Environment). The university has implemented a number of changes to address issues of access and affordability, and SAA determined that a new approach to membership was needed in order to advance the university’s vision and ensure that all new graduates have access to the same opportunities, regardless of ability to pay. To further advance this initiative (while simultaneously supporting SAA’s mission of reaching, serving and engaging all students and alumni), SAA will provide the gift of a lifetime membership to all new graduates, starting with the Class of 2020. SAA has been a member-supported organization since its founding in 1892, and membership fees have helped fund programs, services and opportunities for students and alumni. To fund this gift of membership for future graduating classes, SAA will look to additional income sources or make changes in other areas to offset the lost revenue from membership fees. How do I activate my gifted membership? You do not need to take action to activate your lifetime membership. Your membership will be activated six to eight weeks after your degree is conferred. If you do not see your newly activated membership, contact membership@alumni.stanford.edu. Am I eligible for a refund if I previously purchased a membership? If you are a new graduate (who graduated in the Class of 2020 or later) and previously purchased a membership, you are eligible for a full refund of your membership fees. The refund will be provided to the original purchaser. Contact newgradmembers@alumni.stanford.edu to request a refund. What happens if I don’t request a refund? The fees originally paid for your membership will be used to fund SAA programs that are available to all students and alumni, including STANFORD magazine, alumni email, nationwide engagements like faculty speaker events and time-honored traditions like Reunion Homecoming—meaning you help keep these programs alive for future generations of alumni. Does the refund offer for paid memberships expire? No, eligible paid members (graduates from the Class of 2020 and beyond) may ask for a refund of a life membership purchase at any time, now or in the future. Questions? Email us or call at (650) 725-0692 or at (800) 786-2586 (toll-free). Our business hours are Monday–Friday from 8 a.m.–5 p.m. PT. Need general help with your alumni account or navigating the site? Get help.	common_crawl_stanford.edu_437
Stanford Federal Credit Union financial services Enjoy exceptional service with Stanford Federal Credit Union for home and auto loans, an exclusive alumni debit card and more. All Stanford alumni automatically qualify for these listed offers (and can access them at any time!), while Stanford Alumni Association members also enjoy upgraded versions of select perks and member-only benefits. Enjoy exceptional service with Stanford Federal Credit Union for home and auto loans, an exclusive alumni debit card and more. Providing scientifically based medical information to help you make informed decisions about your health and health care. Take advantage of competitive life and long-term care insurance plans that meet the needs of you and your loved ones. Shop all your Cardinal gear needs, from breathable athletic wear to accessories and more. You have access to money-saving auto and home insurance discounts from Farmers GroupSelect. The Stanford Alumni discount code is FRN. Enjoy greater peace of mind on your travels with a travel insurance plan curated for Stanford alumni and friends. Get high-quality, sustainable, organic coffee delivered to your door, and support alumni and student programs with your purchase. Claim your @alumni.stanford.edu address and let fellow alums and potential employers know you're a Stanford alum, simply by hitting "send." Enjoy a complimentary subscription to the award-winning STANFORD magazine, published five times a year. Reserve your wedding or renewal ceremony at Memorial Church, which welcomes all religious traditions and endeavors. Access alumni-only resources to help you land your first job, make career transitions or grow in your field. Make use of Munzer Business Center office amenities, conference rooms, showers and day lockers, and more. Enjoy seven days (14 days for SAA members) of free access per year at Green Library. Zoom around campus with free bike rentals for you and a guest at the Frances C. Arrillaga Alumni Center. Be well and live healthier with evidence-based classes, apps and coaching packages on a variety of wellness topics. Stanford Alumni Association members unlock access to special member-only benefits, plus upgraded versions of select perks. Preview the exclusive benefits that are especially popular with alums (and make getting an SAA membership the best decision ever). Enjoy access to the private golf course consistently rated as one of the finest in the world. Bring your SAA membership card and another form of ID. Read, research, and learn with free access to searchable databases containing thousands of journals and publications. Log in with your SAA credentials to access the databases.	common_crawl_stanford.edu_438
Stanford Federal Credit Union The Stanford Alumni Association (SAA) has partnered with Stanford Federal Credit Union to bring specialized financial services and discounts to Stanford alumni. Unlike banks, Stanford Federal Credit Union is not-for-profit and owned by its members—so you’ll be able to do more and bank less. In addition to offers that are available to all Stanford alums, SAA members receive $500 off the closing cost of the purchase or refinance of a home loan* and a 0.5% APR discount on auto loans. Featured Offers Explore the products and services Stanford Federal Credit Union offers just for Stanford alumni. Alumni Rewards Visa® Credit Card Choose from custom designs, earn up to 5% cash back and other rewards points, and enjoy no annual or foreign transaction fees. Checking & Savings Account Open a free checking account with free ATMs and add a high-rate Money Market or Certificate. Home Loans Find low rates and fees on purchase and refinance mortgages with an exclusive alumni fee discount, as well as HELOCs and fast approvals. Auto Loans Enjoy low rates, 125% financing, up to 90 days no payments and an exclusive alumni rate discount. Personal Banker Enjoy specialized service from your personal banker whenever you need additional assistance. Jeanine Hansen NMLS #760325 (650) 269-7435 alumni@sfcu.org	common_crawl_stanford.edu_439
Alumni Newsletters Keep up to date on all things alumni with stories, local alumni events, webcasts, faculty talks, and Travel/Study trip information—all sent straight to your inbox. Follow Your Interests Love to learn? Make your emails more relevant by signing up for updates on the topics you most want to hear about. Learn List Receive monthly emails about online learning opportunities like free classes, webcasts featuring Stanford faculty or campus luminaries, and more. STANFORD Magazine Be the first to hear about digital exclusives and more in the online version of STANFORD magazine. Stanford Report Get a daily dose of Stanford News, including research and stories impacting the Stanford community. Travel/Study Looking to travel? Sign up for updates on our amazing journeys, our family-focused trips, or adventures for solo travelers! Travel/Study Receive regular updates on the many programs Travel/Study has to offer, from active adventures to land journeys, sea and river cruises, family trips, and more. Stanford Family Adventures Receive updates on our family-focused trips only. These multi-generational trips include the Young Explorer program for children 6 and up. Solo Travelers Receive periodic updates on trips with availability in single accommodations and notices of last-minute openings on popular, previously sold-out departures. Stanford Sierra Camp Love the mountain air? Sign up for updates on our family getaways, alumni programs, business meetings, and more. Summer Family Camp Be the first to know when applications open for Summer Family Camp—a week in the High Sierra filled with nonstop educational fun, experiences, and memories for the whole family. Healthy Living Retreat for Women Interested in four days of relaxation and rejuvenation with programming presented by Stanford and Bay Area health experts? Join the list to hear when registration opens. Memorial Day Weekend Program Subscribe to get notified when registration opens for this long-weekend program at Fallen Leaf Lake, featuring a faculty lecture, a student performance, a multicourse meal, and more. Stay Current To help you stay in the know, you’re automatically subscribed to these emails once you graduate. The Loop Twice a month, all alumni and parents of current undergraduate students receive The Loop, a curated newsletter that brings you meaningful Stanford stories that can be browsed at any time. Stanford Where You Live Learn about alumni events happening near your primary address and around the world in this popular monthly newsletter. Are you receiving a monthly events newsletter for a region in which you no longer live? Use the link below to update your address. Inbox Looking Lonely? If you haven’t heard from us, your contact information might be out of date or you may have unsubscribed from all alumni emails. First, Check Your Settings Look at your Alumni Account to see if your primary email address is correct. Search your email account to see if Stanford Alumni emails are being filtered into another folder or spam. If your primary email address looks correct but you aren’t receiving emails, you may have unsubscribed. Please contact us to resubscribe.	common_crawl_stanford.edu_440
Stanford Reunion Homecoming Join the Stanford alumni community on the Farm in October! Calling All ’0s and ’5s Come ready to reconnect with old friends, make new ones, and celebrate the shared legacy of your Stanford journey! Stanford’s Reunion Homecoming is a vibrant celebration that invites alumni to reconnect, reminisce, and reignite their Stanford spirit. Over four unforgettable days, attendees can dive into various nostalgic activities such as campus tours, tailgates, and engaging programs designed to foster connections and celebrate shared experiences. Join us for this unforgettable reunion, where laughter, nostalgia, and the Stanford spirit unite to celebrate friendship, community, and lifelong learning. See What’s in Store Revisit campus, reconnect with research, and reunite with friends and classmates. Mini-Reunions Kick off the weekend with Mini-Reunions explicitly tailored for different classes and groups. These gatherings allow classmates to gather and share stories on campus. They also set the stage for reconnecting with old friends and rekindling bonds that have stood the test of time. President’s Welcome and Microlectures Secure a front-row seat at the official reunion welcome and immerse yourself in the latest insights from life and academia. Experience engaging microlectures delivered by esteemed faculty scholars, showcasing groundbreaking research and innovative ideas shaping our future. Class Lunch and Panels The festivities continue with your class lunch and panels. This is a chance to celebrate milestones, reflect on personal journeys, and create new memories together. Please note that some classes do not have a class panel. Check your class page for details. Dinner on the Quad Join us for a captivating evening under the stars, where Stanford alumni of all generations gather in the Quad, filling the air with laughter and the joyous clink of glasses. Your unforgettable night begins with delightful cocktails in Memorial Court, setting the stage for a beautifully curated dinner. Multicultural Alumni Hall of Fame Reception and Ceremony Join us to celebrate the newest inductees to Stanford’s Multicultural Alumni Hall of Fame. The four honorees are selected by the Asian American Activities Center, the Black Community Services Center, El Centro Chicano y Latino, and the Native American Cultural Center for their distinguished service to their communities and society at large. Classes Without Quizzes and Tours Reunion Homecoming offers a unique opportunity to engage in enriching sessions featuring faculty and alumni speakers who delve into various topics. Whether you’re interested in cutting-edge research, inspiring stories, or global issues, there's something for everyone to ignite curiosity and foster intellectual discussions. Find Your Class Find your classmates, volunteer on your class reunion committee, and sign up for Mini-Reunions. Schedule at a Glance Please note that the schedule is subject to change as we finalize details. Thursday, October 16 Afternoon Check-in Open Houses and Receptions Classes Without Quizzes and Tours Evening Volunteer Reception Dinner on the Quad Friday, October 17 Morning Check-in and Continental Breakfast Classes Without Quizzes and Tours President’s Welcome and Microlectures Afternoon Class Lunches and Panels Academic/Departmental Events Open Houses and Receptions Classes Without Quizzes and Tours Cardinal Society Happy Hour Multicultural Alumni Hall of Fame Ceremony and Reception Mini-Reunions Evening Class Parties (10–55th reunions) Saturday, October 18 Morning Check-in and Continental Breakfast Mini-Reunions Classes Without Quizzes and Tours Afternoon Classes Without Quizzes and Tours Evening Class Party (5th and 60th reunion) Time TBD Football vs. Florida State University Class Tailgates Sunday, October 19 Morning University Interfaith Public Worship and Alumni Memorial Service Afternoon Farewell Getting Here Need help navigating lodging and transportation details? Hotels & Lodging The Stanford Alumni Association and HBC Event Services offer discounted accommodations for Stanford Reunion Homecoming. Transportation Get information on the three major airports serving the San Francisco Bay Area, or discover how SAA members can save up to 35% on Avis or Budget car rentals. Interested in attending Stanford Reunion Homecoming? Let everyone know you plan on being there—official registration opens August 12.	common_crawl_stanford.edu_441
45th Reunion The dates are set! Reunion Homecoming 2025 will be held October 16–19, 2025. Save the date, volunteer on your class reunion committee, and confirm your contact information to stay informed! Reconnect This is the time to reconnect with the classmates and places that made Stanford your Stanford. Join Your Class Reunion Committee Spread the word about your reunion or coordinate a Mini-Reunion for your dormmates, teammates, or name-that-groupmates. Mini-Reunions Remember moving into your freshman dorm on your first day on campus? Or the day you arrived at your Overseas Studies destination? Wouldn’t it be great to get together with these friends again? Mini-Reunions will make it happen! 45th Class Book It’s your 45th reunion, and that means it’s almost time to reconnect with your classmates on the pages of your all-digital Reunion Class Book! 45th Reunion Committee See the list of Class of ’80 volunteers contributing their time and creativity to reunite your class. Interested in attending Stanford Reunion Homecoming? Let everyone know you plan on being there—official registration opens August 12. Contact Us Feel free to contact us with questions, feedback, or suggestions. Class Manager Robyn Kamisher robynk@stanford.edu General Inquiries Monday to Friday, 9 a.m.–5 p.m. PT. reunion-info@stanford.edu (650) 725-4239	common_crawl_stanford.edu_442
55th Reunion Committee Many thanks to the Class of 1970 volunteers who are helping to organize or spread the word about Stanford Reunion Homecoming on October 16–19, 2025. Leadership Committee Reunion Co-Chairs Hans Carstensen Susie Phillips Class Event Co-Chairs Ann Craig Hanson Ken Tanaka Class Party Co-Chairs Mike Moore Dave Yancey Special Projects Co-Chairs Bil Barber (Reunion Movie) Ruth Cronkite (TBA) (TBA) (TBA) Social Media Chair Kevin Devine Mini-Reunions Co-Chairs Winnie Moran Jasper Gary Maes (Current Interest Conversations) Pamela Balch Maes (Current Interest Conversations) Berkeley Powell (Current Interest Conversations) Class Correspondent Jenni Bond Volunteer Committee Anne Bauer H. Kirk Brown III Shelley Canter John Eckhouse Connie Evashwick Mary Kircher Hoverson Greg Jacobs Katharine Meyer Lockhart Kathleen Haam Logan Robert Logan Laura Masunaga Cathy Easterbrook Monroe Greg Morris Alex Nicholson Steven Pearson Helena Rosenberg Henry Sayre James Schnieder John Todd Michael Van De Vanter	common_crawl_stanford.edu_443
5th Reunion The dates are set! Reunion Homecoming 2025 will be held October 16–19, 2025. Save the date, volunteer on your class reunion committee, and confirm your contact information to stay informed! Reconnect This is the time to reconnect with the classmates and places that made Stanford your Stanford. Join Your Class Reunion Committee Spread the word about your reunion or coordinate a Mini-Reunion for your dormmates, teammates, or name-that-groupmates. Mini-Reunions Remember moving into your freshman dorm on your first day on campus? Or the day you arrived at your Overseas Studies destination? Wouldn’t it be great to get together with these friends again? Mini-Reunions will make it happen! 5th Class Book It’s your 5th reunion, and that means it’s almost time to reconnect with your classmates on the pages of your all-digital Reunion Class Book! 5th Reunion Committee See the list of Class of ’20 volunteers contributing their time and creativity to reunite your class. Interested in attending Stanford Reunion Homecoming? Let everyone know you plan on being there—official registration opens August 12. Contact Us Feel free to contact us with questions, feedback, or suggestions. Class Manager Caddie Coupe cecoupe@stanford.edu General Inquiries Monday to Friday, 9 a.m.–5 p.m. PT. reunion-info@stanford.edu (650) 725-4239	common_crawl_stanford.edu_444
Class of 2015 - Mini-Reunions Explore the Mini-Reunions for your class below! If you don’t see an organizer for your group or your class group isn’t listed, click here to get involved and help make it happen. Mini-Reunions with the Stanford Band, Counterpoint, Ram’s Head, SLE, Volunteers in Asia, and other groups can be found on the Multi-Year Mini-Reunion page. Updates to individual Mini-Reunions will begin starting on May 1, 2025. Arroyo Organizer: Make it happen Date: TBD Time: TBD Location: TBD Chemical Engineering Organizer: Make it happen Date: TBD Time: TBD Location: TBD Civil & Environmental Engineering Organizer: Make it happen Date: TBD Time: TBD Location: TBD Economics Majors Organizer: Isabella Fu Date: TBD Time: TBD Location: TBD English Majors Organize: Rukma Sen Date: TBD Time: TBD Location: TBD Junipero Organizer: Make it happen Date: TBD Time: TBD Location: TBD Kappa Alpha Theta Organizer: Make it happen Date: TBD Time: TBD Location: TBD Lagunita (West) Adelefa, Eucalipto, Granada Organizer: Make it happen Date: TBD Time: TBD Location: TBD Larkin Organizer: Make it happen Date: TBD Time: TBD Location: TBD Native American Community Organizer: Nick Akiona Date: TBD Time: TBD Location: TBD Overseas - Italy - Florence Organizer: Make it happen Date: TBD Time: TBD Location: TBD Pilipino-American Student Union Organizer: Make it happen Date: TBD Time: TBD Location: TBD Pride Organizer: Joel Kek Date: TBD Time: TBD Location: TBD Product Design Organizer: Make it happen Date: TBD Time: TBD Location: TBD Slavianskii Dom Organizer: Make it happen Date: TBD Time: TBD Location: TBD Stanford Improvisers (SImps) Organizer: Jessica Hoffman Date: TBD Time: TBD Location: TBD Stanford in Cape Town Organizer: Make it happen Date: TBD Time: TBD Location: TBD Stanford in Washington Organizer: Make it happen Date: TBD Time: TBD Location: TBD Trancos Organizer: Nick Akiona Date: TBD Time: TBD Location: TBD Twain Organizer: Make it happen Date: TBD Time: TBD Location: TBD	common_crawl_stanford.edu_445
Class of 1990 - Mini-Reunions Explore the Mini-Reunions for your class below! If you don’t see an organizer for your group or your class group isn’t listed, click here to get involved and help make it happen. Mini-Reunions with the Stanford Band, Counterpoint, Ram’s Head, SLE, Volunteers in Asia, and other groups can be found on the Multi-Year Mini-Reunion page. Updates to individual Mini-Reunions will begin starting on May 1, 2025. A Cappella & Performance Groups (Dollies, Everyday People, Fleet Street, Harmonics, LSJUMB (The Band), Mendicants, Mixed Company, Ram's Head, Talisman, Testimony, O-Tone A) Organizer: Set Soeun Date: TBD Time: TBD Location: TBD Alondra Organizer: Anne Chun Longo, Rob Mihalko Date: TBD Time: TBD Location: TBD Branner Organizer: Make it happen Date: TBD Time: TBD Location: TBD Casa Zapata Organizer: Make it happen Date: TBD Time: TBD Location: TBD Cedro Organizer: Make it happen Date: TBD Time: TBD Location: TBD Chaparral Organizer: Make it happen Date: TBD Time: TBD Location: TBD Donner Organizer: Patricia Chang Date: TBD Time: TBD Location: TBD East Organizer: Make it happen Date: TBD Time: TBD Location: TBD Eucalipto Organizer: Make it happen Date: TBD Time: TBD Location: TBD Larkin Organizer: Make it happen Date: TBD Time: TBD Location: TBD Madera Organizer: Make it happen Date: TBD Time: TBD Location: TBD NROTC Organizer: Make it happen Date: TBD Time: TBD Location: TBD Okada Organizer: Make it happen Date: TBD Time: TBD Location: TBD Otero Organizer: Make it happen Date: TBD Time: TBD Location: TBD Overseas - Britain - Oxford Organizer: Make it happen Date: TBD Time: TBD Location: TBD Overseas - Germany - Berlin Organizer: Make it happen Date: TBD Time: TBD Location: TBD Overseas - Greece Organizer: Make it happen Date: TBD Time: TBD Location: TBD Overseas - Italy - Florence (Jan. - Jun. 1989) & (Jan. - Mar. 1991) Organizer: David Ciulla, Christina Fuller Larsen Date: TBD Time: TBD Location: TBD Paloma Organizer: Make it happen Date: TBD Time: TBD Location: TBD Regional Meet-Ups Organizer: Make it happen Date: TBD Time: TBD Location: TBD Rinconada Organizer: Make it happen Date: TBD Time: TBD Location: TBD Sierra Camp Counselors Organizer: Make it happen Date: TBD Time: TBD Location: TBD Sigma Nu Organizer: Make it happen Date: TBD Time: TBD Location: TBD Soto Organizer: Make it happen Date: TBD Time: TBD Location: TBD Stanford Symphony Orchestra Organizer: Make it happen Date: TBD Time: TBD Location: TBD Trancos Organizer: Make it happen Date: TBD Time: TBD Location: TBD Twain Organizer: Make it happen Date: TBD Time: TBD Location: TBD	common_crawl_stanford.edu_446
Campus Amenities Wi-Fi Access Wi-Fi access to the university’s online resources is widely available across campus, including all academic and administrative buildings. Frances C. Arrillaga Alumni Center Visit your home away from home on the Stanford campus. Conveniently located on the corner of Campus Dr. and Galvez St., ‘Fran’ offers a variety of spaces and services to make your visit comfortable and productive. Open Hours Thursday, October 16 Time: TBD Friday, October 17 Time: TBD Saturday, October 18 Time: TBD Sunday, October 19 Time: TBD Gym/Recreation Facilities Step 1: For an efficient check-in process, attendees should create a new user account prior to their arrival by visiting recwell.stanford.edu. If a new user account is not created beforehand, one must be created with the front desk staff at the time of arrival. Step 2: Attendees must present their alumni reunion badge to the facility front desk staff to qualify for the promo code used to purchase the complementary multi-day pass. If a name tag badge is not presented, attendees will be asked to pick up their badge first before returning to the facilities. Attendees should also be ready to present some form of identification if a user account was not created in advance. Step 3: Once attendees are on campus with their reunion badge in hand, they can visit Athletics facilities to have the promo code applied to the purchase of their multi-day pass. Promo code limits apply - one per user account. More information regarding Athletics facilities and hours of operation can be found here. Step 4 (Optional): After creating their accounts and applying the promo code for the passes, Athletics staff can help alums download the mobile Stanford RecWell App, which can be used to scan into Athletics facilities. Apple App Store Google Play Store RecWell App Login Steps for Access Dining on Campus Stanford Dining prides itself on providing high-quality and nutritious cuisine to meet the wide variety of dietary needs within the Stanford community. You can also view this map of additional Stanford eateries. Explore Campus Explore the Stanford campus in person and online with self-guided tours and virtual experiences.	common_crawl_stanford.edu_447
Explore Campus Explore Stanford's museums, exhibits and other favorite campus spots during your free time. Arizona Cactus Garden The garden, also known as the Cactus Garden, was designed for Jane and Leland Stanford by landscape architect Rudolf Ulrich between 1881 and 1883. During the early years of the university, the Cactus Garden became the meeting place for many courting Stanford students. Athletics Facilities Step 1: For an efficient check-in process, attendees should create a new user account prior to their arrival by visiting recwell.stanford.edu. If a new user account is not created beforehand, one must be created with the front desk staff at the time of arrival. Step 2: Attendees must present their alumni reunion badge to the facility front desk staff to qualify for the promo code used to purchase the complementary multi-day pass. If a name tag badge is not presented, attendees will be asked to pick up their badge first before returning to the facilities. Attendees should also be ready to present some form of identification if a user account was not created in advance. Step 3: Once attendees are on campus with their reunion badge in hand, they can visit Athletics facilities to have the promo code applied to the purchase of their multi-day pass. Promo code limits apply - one per user account. More information regarding Athletics facilities and hours of operation can be found here. Step 4 (Optional): After creating their accounts and applying the promo code for the passes, Athletics staff can help alums download the mobile Stanford RecWell App, which can be used to scan into Athletics facilities. Apple App Store Google Play Store RecWell App Login Steps for Access Stanford Bookstore Purchase some Stanford swag while on campus. Visit the bookstore website for open hours and more information. Lake Lagunita It may be dry, but Lake Lagunita is still a beautiful place to go for a run, walk or just enjoy the view. McMurtry Building The McMurtry Building opened for classes in Fall 2015. Housed within 96,000 square feet of this academic building are programs in art practice, design, art history, film and media studies, and documentary film and video. Meyer Green Stanford's newest open space features curving walkways and gentle grassy slopes surrounded by groves of eucalyptus and cedar. Papua New Guinea Sculpture Garden This garden contains 40 wood and stone carvings of people, animals, and magical beings that illustrate creation stories and cultural traditions. Ten artists from the inland Sepik River area created the sculptures on-site during a five-month visit in 1994. Stanford Athletics Home of Champions Museum The Home of Champions explores the history and legacy of Stanford Athletics, with static and interactive exhibits. Hear the stories of student-athletes, and see the Axe, the Heisman Trophy, and more. Stanford Dish See gorgeous views of campus and the bay on this 3.5-mile trail. No pets or animals allowed within the Dish Area except trained service animals. Stanford Mausoleum and Angel of Grief Visit the Stanford's final resting place and surrounding sculptures. Terman Fountain Visit a new fountain on campus. This is a popular location for students to wade and relax in while on campus. Stanford Soundwalk The COVID Memorial Soundwalk is a walking route on the Stanford campus, accompanied by a specially selected playlist of music performed by Stanford faculty and student musicians. The walk starts at 1 University Ave in Palo Alto at the Stanford gate. Explore Stanford's expansive public art collection on campus or virtually with our newest mobile features: Public Art Tours and Art Map.	common_crawl_stanford.edu_448
Maps & Directions Stanford University Searchable Campus Map Online searchable map of campus addresses and building locations. Outdoor Arts Map Comprehensive online map of outdoor art, art venues and art departments on campus. Directions From Highway 101 North & South Exit onto Embarcadero Road and travel west, following the signs directing you to Stanford University. About three miles after exiting the freeway, Embarcadero Road becomes Galvez Street as you cross El Camino Real. Stay in the left lane and continue past the stadium. Follow the Reunion Homecoming parking signs or view the Building, Parking and Shuttle Map (available in September) to locate the appropriate parking lot. From Highway 280 North & South Exit onto Sand Hill Road and follow the signs for Stanford University. Heading east, drive approximately three miles to the Stanford Shopping Center. Turn right onto Arboretum Road (Nordstrom is on your right). Stay on Arboretum until it ends, then turn right onto Galvez Street. Move to the left lane and continue past the stadium. Follow the Reunion Homecoming parking signs or view the Building, Parking and Shuttle Map (available in September) to locate the appropriate parking lot.	common_crawl_stanford.edu_449

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