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util.py
'''
File: Util
----------
This file contains several helper methods and a Belief class that you
can (and should) use to answer the various parts of the Driverless
Car assignment. Read each method description!
In addition to the Belief class, this file contains the
following helper methods:
saveTransProb()
loadTransProb()
xToCol(x)
yToRow(y)
colToX(col)
rowToY(row)
pdf(mean, std, value)
weightedRandomChoice(weightDict)
Licensing Information: Please do not distribute or publish solutions to this
project. You are free to use and extend Driverless Car for educational
purposes. The Driverless Car project was developed at Stanford, primarily by
Chris Piech (piech@cs.stanford.edu). It was inspired by the Pacman projects.
'''
from engine.const import Const
import cPickle as pickle
import math
import os.path
# Function: Save Trans Prob
# -------------------------
# Saves the transition probabilities that have been generated by running
# "learner." The transDict can by a dictionary of any type that you design.
# For example it could be a dictionary of tuples that are associated with
# their own dictionaries.
def saveTransProb(transDict, transFile):
pickle.dump(transDict, transFile)
# Function: Load Trans Prob
# -------------------------
# Loads the transition probabilities that have been generated by running
# "learner."
def loadTransProb():
transFileName = Const.WORLD + 'TransProb.p'
transFilePath = os.path.join('learned', transFileName)
with open(transFilePath) as transFile:
return pickle.load(transFile)
raise Exception('could not load ' + transFilePath + '. Did you run learner on this layout?')
# Function: X to Col
# -------------------------
# Returns the col in the discretized grid, that the value x falls into.
# This function does not check that x is in bounds.
# Warning! Do not confuse rows and columns!
def xToCol(x):
return int((x / Const.BELIEF_TILE_SIZE))
# Function: Y to Row
# -------------------------
# Returns the row in the discretized grid, that the value y falls into.
# This function does not check that y is in bounds.
# Warning! Do not confuse rows and columns!
def yToRow(y):
return int((y / Const.BELIEF_TILE_SIZE))
# Function: Row to y
# -------------------------
# Returns the y value of the center of a tile in row in the discretized grid.
# This function does not check that row is in bounds.
# Warning! Do not confuse x and y!
def rowToY(row):
return (row + 0.5) * Const.BELIEF_TILE_SIZE
# Function: Col to x
# -------------------------
# Returns the x value of the center of a tile in col in the discretized grid.
# This function does not check that col is in bounds.
# Warning! Do not confuse x and y!
def colToX(col):
return (col + 0.5) * Const.BELIEF_TILE_SIZE
# Function: Pdf
# -------------------------
# Returns the Guassian (aka Normal) probability density of a distribution with
# a given mean and std producing a given value.
def pdf(mean, std, value):
u = float(value - mean) / abs(std)
y = (1.0 / (math.sqrt(2 * math.pi) * abs(std))) * math.exp(-u * u / 2.0)
return y
# Function: Weighted Random Choice
# --------------------------------
# Given a dictionary of the form element -> weight, selects an element
# uniformly over the different weights.
def weightedRandomChoice(weightDict):
weights = []
elems = []
for elem in dist:
weights.append(dist[elem])
elems.append(elem)
total = sum(weights)
key = random.uniform(0, total)
runningTotal = 0.0
chosenIndex = None
for i in range(len(weights)):
weight = weights[i]
runningTotal += weight
if runningTotal > key:
chosenIndex = i
return elems[chosenIndex]
raise Exception('Should not reach here')
# Class: Belief
# ----------------
# This class represents the belief for a single inference state of a single
# car. It has one belief value for every tile on the map. You *must* use
# this class to store your belief values. Not only will it break the
# visualization and simulation control if you use your own, it will also
# break our autograder :).
class Belief(object):
# Function: Init
# --------------
# Constructor for the Belief class. It creates a belief grid which is
# numRows by numCols. As an optional third argument you can pass in a the
# initial belief value for every tile (ie Belief(3, 4, 0.0) would create
# a belief grid with dimensions (3, 4) where each tile has belief = 0.0.
def __init__(self, numRows, numCols, value = None):
self.numRows = numRows
self.numCols = numCols
numElems = numRows * numCols
if value == None:
value = (1.0 / numElems)
self.grid = [[value for _ in range(numCols)] for _ in range(numRows)]
self.sum = value * numCols * numRows
# Function: Set Prob
# ------------------
# Sets the probability of a given row, col to be p
def setProb(self, row, col, p):
oldP = self.getProb(row, col)
delta = p - oldP
self.grid[row][col] = p
self.sum += delta
# Function: Add Prob
# ------------------
# Increase the probability of row, col by delta. Belief probabilities are
# allowed to increase past 1.0, but you must later normalize.
def addProb(self, row, col, delta):
self.grid[row][col] += delta
self.sum += delta
assert self.grid[row][col] >= 0.0
# Function: Get Prob
# ------------------
# Returns the belief for tile row, col.
def getProb(self, row, col):
return self.grid[row][col]
# Function: Normalize
# ------------------
# Makes the sum over all beliefs 1.0 by dividing each tile by the total.
def normalize(self):
for r in range(self.numRows):
for c in range(self.numCols):
self.grid[r][c] /= self.sum
self.sum = 1.0
# Function: Get Num Rows
# ------------------
# Returns the number of rows in the belief grid.
def getNumRows(self):
return self.numRows
# Function: Get Num Cols
# ------------------
# Returns the number of cols in the belief grid.
def getNumCols(self):
return self.numCols
# Function: Get Sum
# ------------------
# Return the sum of all the values in the belief grid. Used to make sure
# that the matrix has been normalized.
def getSum(self):
return self.sum
|
common_crawl_stanford.edu_50
|
News Feed
Course Information
Instructor: Chris Piech
Course assistants:
Contact: Please use Piazza for all questions related to lectures, homeworks, and projects. For private questions, email: cs221-sum1213-staff@lists.stanford.edu.
Office Hours: See the office hour calendar. Additional office hours are also availible by appointment.
Book: Russell and Norvig. Artificial Intelligence: A Modern Approach, 3rd. edition.
Lectures: If you can make it to lecture come! You learn a lot from your peers and stay on track. The lectures are recorded -- so if you can't make it, or if you want to see old lectures, you can find them at myvideosu.
Prerequisites: a good grasp of basic data structures and algorithms, probability, linear algebra; solid programming skills.
Tentative Schedule
Policies
You are encouraged to talk about the homework assignments in small groups. However your final submission must be written up in your own words and you must not share actual lines of code with other students.
You have three six "late" days for the entire quarter. You can use each late day to grant yourself a 24 hour extension.
Late days either apply to an entire problem set (even though you submit each part seperately) or a programming assignment. You can use up to three
late days on an assignment. After you have used up all of your late days we will no longer accept late submissions. If an unforseen circumstance comes up
and you need an extension beyond your late days, contact Chris.
Homework assignments will have specific submission instructions included with the handouts. We will use a certain amount of automatic grading to help us deal with the massive amounts of code everyone submits, so please follow the submission instructions exactly as written!
You are free to discuss the assignment and solutions with others. However, you must write your own assignment, and must not represent any portion of others' work as your own. Anybody violating the honor code will be referred to the Judical-Affairs Office. If convicted, the normal penalty is a quarter suspension or worse.
|
common_crawl_stanford.edu_51
|
Adversarial Texture Optimization from RGB-D Scans
Stanford University
2Technical University of Munich
3Google Research
4UC Berkeley
IEEE Conference on Computer Vision and Pattern Recognition 2020 (CVPR 2020)
Abstract
Realistic color texture generation is an important step in RGB-D surface reconstruction, but remains challenging in practice due to inaccuracies in reconstructed geometry, misaligned camera poses, and view-dependent imaging artifacts. In this work, we present a novel approach for color texture generation using a conditional adversarial loss obtained from weakly-supervised views. Specifically, we propose an approach to produce photorealistic textures for approximate surfaces, even from misaligned images, by learning an objective function that is robust to these errors. The key idea of our approach is to learn a patch-based conditional discriminator which guides the texture optimization to be tolerant to misalignments. Our discriminator takes a synthesized view and a real image, and evaluates whether the synthesized one is realistic, under a broadened definition of realism. We train the discriminator by providing as `real' examples pairs of input views and their misaligned versions -- so that the learned adversarial loss will tolerate errors from the scans. Experiments on synthetic and real data under quantitative or qualitative evaluation demonstrate the advantage of our approach in comparison to state of the art.
|
common_crawl_stanford.edu_52
|
A unique aspect of human visual understanding is the ability to flexibly interpret abstract concepts: acquiring lifted rules explaining what they symbolize, grounding them across familiar and unfamiliar contexts, and making predictions or reasoning about them. While off-the-shelf vision-language models excel at making literal interpretations of images (e.g., recognizing object categories such as tree branches), they still struggle to make sense of such visual abstractions (e.g., how an arrangement of tree branches may form the walls of a maze). To address this challenge, we introduce Deep Schema Grounding (DSG), a framework that leverages explicit structured representations of visual abstractions for grounding and reasoning. At the core of DSG are schemas—dependency graph descriptions of abstract concepts that decompose them into more primitive-level symbols. DSG uses large language models to extract schemas, then hierarchically grounds concrete to abstract components of the schema onto images with vision-language models. The grounded schema is used to augment visual abstraction understanding. We systematically evaluate DSG and different methods in reasoning on our new Visual Abstractions Dataset, which consists of diverse, real-world images of abstract concepts and corresponding question-answer pairs labeled by humans. We show that DSG significantly improves the abstract visual reasoning performance of vision-language models, and is a step toward human-aligned understanding of visual abstractions.
Humans possess the remarkable ability to flexibly acquire and apply abstract concepts when interpreting the concrete world around us. Consider the concept "maze": our mental model can interpret mazes constructed with conventional materials (e.g., drawn lines) or unconventional ones (e.g., icing), and reason about mazes across a wide range of configurations and environments (e.g., in a cardboard box or on a knitted square). Our goal is to build systems that can make such flexible and broad generalizations as humans do. This necessitates a reconsideration of a fundamental question: what makes a maze look like a maze? A maze is not defined by concrete visual features such as the specific material of walls or particular perpendicular intersections, but by lifted rules over symbols—a plausible model for a maze includes its layout, the walls, and the designated entry and exit.
Current VLMs often struggle to reason about visual abstractions at a human level, frequently defaulting to literal interpretations of images, such as a collection of object categories. Here, we propose Deep Schema Grounding (DSG), a framework for models to interpret visual abstractions. At the core of DSG are schemas—a dependency graph description of abstract concepts. Schemas characterize common patterns that humans use to interpret the visual world, generalize efficiently from limited data, and reason across multiple levels of abstraction for flexible adaptation. A schema for "helping" allows us to understand relations between characters in a finger puppet scene, while a schema for "tic-tac-toe" allows us to play the game even when the grid is composed of hula hoops instead of drawn lines. A schema for "maze" makes a maze look like a maze.
Deep Schema Grounding (DSG) explicitly uses schemas generated by and grounded by large pretrained models to reason about visual abstractions. Concretely, we model schemas as programs encoding directed acyclic graphs (DAGs), which decompose an abstract concept into a set of more concrete visual concepts as subcomponents. The full framework is composed of three steps.
1. First, we extract schema definitions of abstract concepts from a LLM.
2. Next, DSG hierarchically queries a VLM, first grounding concrete symbols in the DAG (i.e., symbols that do not depend on the interpretation of other symbols), then using those symbols as conditions to ground more abstract symbols.
3. Finally, we use the resolved schema, including the grounding of all its components, as an additional context into a vision-language model to improve visual reasoning.
Our method is a general framework for abstract concepts that does not depend on specific models; the LLMs and VLMs used are interchangeable.
To investigate the capabilities of models in understanding abstract concepts, we introduce the Visual Abstractions Dataset (VAD). VAD is a visual question-answering dataset that consists of diverse, real-world images representing abstract concepts. The abstract concepts span 4 different categories: strategic concepts that are characterized by rules and patterns (e.g., "tic-tac-toe"), scientific concepts of phenomena that cannot be visualized in their canonical forms (e.g., "atoms"), social concepts that are defined by theory-of-mind relations (e.g., "deceiving"), and domestic concepts of household objectives that cannot be directly defined by specific arrangements of objects (e.g, "table setting for two").
Each image is an instantiation of an abstract concept, and is paired with questions that probe understanding of the visual abstraction; for example, "Imagine that the image represents a maze. What is the player in this maze?" The VAD comprises 540 of such examples, with answers labeled by 5 human annotators from Prolific.
We evaluate Deep Schema Grounding on the Visual Abstractions Dataset, and show that DSG consistently improves performance of vision-language models across question types, abstract concept categories, and base models. Notably, DSG improves GPT-4o by 6.6 percent points overall (↑ 9.9% relative improvement), and, in particular, demonstrates a 10 percent point improvement (↑ 16.6% relative improvement) in questions that involve counting.
Below, we show examples of schemas for concepts across categories in the Visual Abstractions Dataset, as well as the visual features that they may be grounded to.
|
common_crawl_stanford.edu_53
|
Figure 1. From extra-professional research, published in the 2016 American Alpine Journal [
link].
Johan Ugander
Associate Professor
Management Science & Engineering (MS&E)
Institute for Computational & Mathematical Engineering (ICME)
Cisco Systems Faculty Scholar
School of Engineering
Stanford University
I use statistical and computational methods to study social networks, human behavior, and their interplay.
My work is partly empirical, working to advance our understanding of important social systems and processes, and partly methodological, developing tools to do so more efficiently and effectively.
I frequently leverage the unique measurement opportunities created by the internet and digitization over the last two decades, making it possible to study social and individual behavior in previously unprecedented ways.
Within MS&E I am a member of the Social Algorithms Lab (SOAL). I am also among the faculty co-directors of the RAIN Seminar.
At Stanford I am also affiliated with the Institute for Computational & Mathematical Engineering (ICME) and the Center for Computational Social Science. At the undergraduate level I also advise students from the Symbolic Systems (SymSys) and Mathematical and Computational Science (MCS) majors.
I obtained my Ph.D. in Applied Mathematics from Cornell University in 2014, advised by Jon Kleinberg.
I also hold degrees from the University of Cambridge and Lund University; before that I attended Deep Springs College.
From 2010-14 I held an affiliation with the Facebook Data Science team.
In 2014-15 I spent one year as a post-doctoral researcher at Microsoft Research, hosted by Eric Horvitz. I joined the Stanford faculty in September 2015 and received tenure in June 2022.
[third person bio] [serious photo]
Contact me: jugander {at} stanford.edu
Visiting address: Huang Engineering Center 357, 475 Via Ortega, Stanford, CA 94305-4121
See also: mastodon, twitter, medium, research blog
I am on sabbatical for the 2024-25 academic year, visiting Yale Statistics & Data Science (S&DS) and the Yale Institute for Foundations of Data Science (FDS). For Stanford undergraduate advising inquiries, please contact student services for your respective program (MS&E, MCS/Data Science, SymSys, etc). For current Stanford PhD student inquiries, I will not be advising rotations during the 2024-25 year.
Upcoming talks
- Fall 2024: Vermont Statistics (Oct 8), CODE@MIT (Oct 19-20), CornellTech (Oct 28), CMU MLD (Nov 19), Cornell CAM (Dec 6)
- Spring 2025: Columbia Statistics (Feb 3), MIT IDSS (Mar 3), Northeastern NetSI (Apr 4).
News
- Spring 2024: Teaching a PhD semiar, MS&E334: Topics in Social Data, with Ramesh Johari.
- Winter 2024: Teaching MS&E135: Networks at the undergraduate level.
- Fall 2023: Teaching MS&E231: Social Algorithms at the masters level.
- Fall 2023: Talks at National Academies of Science, Collective Intelligence/HCOMP, UC Berkeley Haas.
- Spring 2023: On Sabbatical. Talks at KTH, University of Copehagen.
- Winter 2023: Teaching MS&E135: Networks at the undergraduate level.
- Fall 2022: Teaching MS&E231: Introduction to Computational Social Science at the masters level.
- Winter 2022: Teaching MS&E135: Networks at the undergraduate level and MS&E234: Data Privacy and Ethics at the masters level.
- Fall 2021: Talks at Texas A&M (9/27, zoom), Princeton (12/6), NeurIPS Workshop on Human and Machine Decisions (12/14, zoom), CMStats (12/18, zoom).
- November 2021: New paper in PNAS studying diffusion cascades.
- Summer 2021: Two new papers at ICWSM (one winning an Oustanding Paper Award), one at KDD.
- May 2021: New paper in Management Science on evaluating network inteventions.
Ph.D. students and post-docs
Former students/postdocs:
Amel Awadelkarim (PhD ICME, 2024),
Serina Chang (PhD CS, 2024, co-advised w/ Leskovec),
Samir Khan (PhD Statistics, 2024),
Zhaonan Qu (Post-doc 2023-2024, co-advised w/ Imbens),
Martin Saveski (Post-doc 2020-23),
Kevin Han (PhD Statistics, 2023, co-advised w/ Imbens),
Jenny Hong (PhD MS&E, 2023, co-advised w/ Manning),
Jan Overgoor (PhD MS&E, 2021),
Arjun Seshadri (PhD EE, 2021),
Imanol Arrieta Ibarra (PhD MS&E, 2020),
Kristen Altenburger (PhD MS&E, 2020),
Stephen Ragain (PhD MS&E, 2019),
Alex Chin (PhD Statistics, 2019).
Publications
See also my
Google Scholar profile.
Pre-prints:
-
K Tomlinson, J Ugander, J Kleinberg
Exclusion Zones of Instant Runoff Voting
arXiv:2502.16719, last updated March 2025.
-
I Aguiar, P Chodrow, J Ugander
The illusion of households as entities in social networks
arXiv:2502.14764, last updated February 2025.
-
I Slaughter, A Peytavin, J Ugander, M Saveski
Community Notes Moderate Engagement With and Diffusion of False Information Online
arXiv:2502.13322, last updated February 2025.
-
M Eichhorn, S Khan, J Ugander, C Lee Yu
Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference
arXiv:2405.07979, last updated July 2024.
-
D Liu, A Seshadri, T Eliassi-Rad, J Ugander
Re-visiting Skip-Gram Negative Sampling: Dimension Regularization for More Efficient Dissimilarity Preservation in Graph Embeddings
arXiv:2405.00172, last updated April 2024.
Publications:
-
Z Qu, A Galichon, W Gao, J Ugander
On Sinkhorn's Algorithm and Choice Modeling
To appear, Operations Research, 2025+.
[code]
-
K Tomlinson, T Namjoshi, J Ugander, J Kleinberg
Replicating Electoral Success
To appear, Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI), 2025.
[code]
-
J Ugander, Z Epstein
The Art of Randomness: Sampling and Chance in the Age of Algorithmic Reproduction
Harvard Data Science Review, 2024.
-
A Awadelkarim, J Ugander
Statistical Models of Top-k Partial Orders
Proc. 30th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2024.
[code]
-
S Chang, F Koehler, Z Qu, J Leskovec, J Ugander
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
International Conference on Machine Learning (ICML), 2024.
[code]
- S Khan, M Saveski, J Ugander
Off-policy evaluation beyond overlap: partial identification through smoothness
International Conference on Machine Learning (ICML), 2024.
[code]
-
I Aguiar, D Taylor, J Ugander
A tensor factorization model of multilayer network interdependence
Journal of Machine Learning Research (JMLR), 2024.
[code]
[twitter thread]
[arxiv]
-
S Khan, J Ugander
Doubly-robust and heteroscedasticity-aware sample trimming for causal inference
Biometrika, 2024.
[code]
[twitter thread]
[arxiv]
-
I Aguiar, J Ugander
The latent cognitive structures of social networks
Network Science, 2024.
[code]
[arXiv]
- K Tomlinson, J Ugander, J Kleinberg
The Moderating Effect of Instant Runoff Voting
Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI), 2024.
[code]
- M Saveski, S Jecmen, N Shah, J Ugander
Counterfactual Evaluation of Peer-Review Assignment Policies
Advances in Neural Information Processing Systems (NeurIPS), 2023.
[code]
[NeurIPS video]
- M Bernstein, A Christin, J Hancock, T Hashimoto, C Jia, M Lam, N Meister, N Persily, T Piccardi, M Saveski, J Tsai, J Ugander, C Xu
Embedding Societal Values into Social Media Algorithms
Journal of Online Trust and Safety, 2(1), 2023.
-
K Han, J Ugander
Model-Based Regression Adjustment with Model-Free Covariates for Network Interference
Journal of Causal Inference, 2023.
[arXiv]
- A Awadelkarim, A Seshadri, I Ashlagi, I Lo, J Ugander
Rank-heterogeneous Preference Models for School Choice
Proc. 29th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2023.
[code]
[KDD video]
[twitter thread]
- K Tomlinson, J Ugander, J Kleinberg
Ballot length in instant runoff voting
Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.
[code]
[twitter thread]
- S Chang, D Vrabac, J Leskovec, J Ugander
Estimating geographic spillover effects of COVID-19 policies from large-scale mobility networks
Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.
[code]
[twitter thread]
-
J Ugander, H Yin
Randomized Graph Cluster Randomization
Journal of Causal Inference, 2023.
[code]
[arXiv]
[talk video, MIT IDE]
[twitter thread]
-
S Khan, J Ugander
Adaptive normalization for IPW estimation
Journal of Causal Inference, 2023.
[code]
[arXiv]
[talk slides, UChicago]
[twitter thread]
-
C Musco, I Ramesh, J Ugander, RT Witter
How to Quantify Polarization in Models of Opinion Dynamics
KDD Workshop on Mining and Learning with Graphs (MLG), 2022.
-
S Chang, J Ugander
To Recommend or Not? A Model-Based Comparison of Item-Matching Processes
Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2022.
- J Juul, J Ugander
Comparing information diffusion mechanisms by matching on cascade size
Proceedings of the National Academy of Sciences (PNAS), 118(46), 2021.
[code]
[Stanford News]
[twitter thread]
- K Tomlinson, J Ugander, A Benson
Choice Set Confounding in Discrete Choice
Proc. 27th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2021.
[code]
- K Altenburger, J Ugander
Which Node Attribute Prediction Task Are We Solving? Within-Network, Across-Network, or Across-Layer Tasks
Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2021.
(Outstanding Problem-Solution Paper Award)
[code]
- W Cai, J Ugander
Experience-Driven Peer Effects: Evidence from a Large Natural Experiment
Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2021.
[code]
- A Chin, D Eckles, J Ugander
Evaluating stochastic seeding strategies in networks
Management Science, 2021.
[arxiv pre-print]
[code]
[twitter thread]
-
A Seshadri, S Ragain, J Ugander
Learning Rich Rankings
Advances in Neural Information Processing Systems (NeurIPS) 33, 2020.
[code]
[twitter thread]
-
J Overgoor, G Supaniratisai, J Ugander
Scaling Choice Models of Relational Social Data
Proc. 26th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2020.
[talk slides, SIAMNS by Jan Overgoor]
[code]
[twitter thread]
-
A Awadelkarim, J Ugander
Prioritized Restreaming Algorithms for Balanced Graph Partitioning
Proc. 26th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2020.
[talk slides, SIAMNS by Amel Awadelkarim]
[code]
[twitter thread]
-
J Su, K Kamath, A Sharma, J Ugander, S Goel
An Experimental Study of Structural Diversity in Social Networks
Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2020.
(Best Paper Award)
[talk slides, CODE@MIT17 by Jessica Su]
[arXiv pre-print]
[twitter thread]
-
H Yin, A Benson, J Ugander
Measuring Directed Triadic Closure with Closure Coefficients
Network Science, 2020.
[code] [arXiv pre-print]
-
A Seshadri, J Ugander
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice
Extended abstract, ACM Conference on Economics and Computation (EC), 2019.
[talk video, EC]
[talk slides, EC by Arjun Seshadri]
[twitter thread]
-
A Seshadri, A Peysakhovich, J Ugander
Discovering Context Effects from Raw Choice Data
International Conference on Machine Learning (ICML), 2019.
[talk slides, ICML by Arjun Seshardi]
[code]
[twitter thread]
-
J Overgoor, A Benson, J Ugander
Choosing To Grow a Graph: Modeling Network Formation as Discrete Choice
Proceedings of the World Wide Web Conference (WWW), 2019.
[talk slides, NetSci19 by Austin Benson]
[code]
[twitter thread]
-
A Chin, Y Chen, KM Altenburger, J Ugander
Decoupled smoothing on graphs
Proceedings of the World Wide Web Conference (WWW), 2019.
[talk slides, WWW by Yatong Chen]
[code]
-
R Makhijani, J Ugander
Parametric Models for Intransitivity in Pairwise Rankings
Proceedings of the World Wide Web Conference (WWW), 2019.
-
I Arrieta-Ibarra, J Ugander
A Personalized BDM Mechanism for Efficient Market Intervention Experiments
Proc. 19th ACM Conf. on Economics and Computation (EC), 2018.
[talk slides, EC by Imanol Arrieta-Ibarra]
[code]
-
KM Altenburger, J Ugander
Monophily in social networks introduces similarity among friends-of-friends
Nature Human Behaviour 2:284–290, 2018.
[Supplementary Information]
[NHB page]
[arXiv pre-print]
[code]
-
B Fosdick, D Larremore, J Nishimura, J Ugander
Configuring Random Graph Models with Fixed Degree Sequences
SIAM Review 60(2):315–355, 2018.
[talk slides, NetSci17 by Dan Larremore]
[arXiv pre-print]
[code]
-
J Kleinberg, S Mullainathan, J Ugander
Comparison-Based Choices
Proc. 18th ACM Conf. on Economics and Computation (EC), 2017.
[talk slides, EC]
[talk video, EC]
- D Eckles, B Karrer, J Ugander
Design and analysis of experiments in networks: Reducing bias from interference
Journal of Causal Inference 5(1):1-23, 2017.
[arXiv pre-print]
[talk slides, CODE@MIT14]
-
I Kloumann, J Ugander, J Kleinberg
Block models and personalized PageRank
Proceedings of the National Academy of Sciences (PNAS) 114(1):33-38, 2017.
[talk slides, Google Research] [arXiv pre-print]
-
S Ragain, J Ugander
Pairwise Choice Markov Chains
Advances in Neural Information Processing Systems (NeurIPS) 29, 2016.
[talk slides, CODE@MIT17] [Code and data]
-
J Ugander, R Drapeau, C Guestrin
The Wisdom of Multiple Guesses
Proc. 16th ACM Conf. on Economics and Computation (EC), 2015.
[talk slides, EC] [Code and data]
-
AZ Jacobs, SF Way, J Ugander, A Clauset
Assembling thefacebook: Using Heterogeneity to Understand Online Social Network Assembly
Proc. 7th ACM Int'l Conf. on Web Science (WebSci), 2015.
[talk slides, ICCSS16 by Abigail Jacobs]
[Supplementary data]
- J Ugander, B Karrer, L Backstrom, J Kleinberg
Graph Cluster Randomization: Network Exposure to Multiple Universes
Proc. 19th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2013.
[talk video, KDD]
- J Nishimura, J Ugander
Restreaming Graph Partitioning: Simple Versatile Algorithms for Advanced Balancing
Proc. 19th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2013.
[Cython implementation by Justin Vincent]
- J Ugander, L Backstrom, J Kleinberg
Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph Collections
Proc. 22nd Int'l World Wide Web Conf. (WWW), 2013.
[talk slides, WWW]
[Summary and R code]
[talk video by Jon Kleinberg]
- DM Romero, C Tan, and J Ugander
On the Interplay Between Social and Topical Structure
Proc. 7th AAAI Int'l Conf. on Weblogs and Social Media (ICWSM), 2013.
[talk slides, ICWSM by Chenhao Tan]
- J Ugander, L Backstrom
Balanced Label Propagation for Partitioning Massive Graphs
Proc. 6th ACM Int'l Conf. on Web Search and Data Mining (WSDM), 2013.
(Best Student Paper Award)
[talk slides, WSDM]
- J Ugander, L Backstrom, C Marlow, J Kleinberg
Structural Diversity in Social Contagion
Proceedings of the National Academy of Sciences (PNAS), 109(16) 5962-5966, 17 April 2012.
[talk slides, NetSci]
- J Ugander, B Karrer, L Backstrom, C Marlow
The Anatomy of the Facebook Social Graph.
arXiv, 2011.
- L Backstrom, P Boldi, M Rosa, J Ugander, S Vigna
Four Degrees of Separation
Proc. 4th ACM Int'l Conf. on Web Science (WebSci), 2012.
(Best Paper Award)
[HyperANF metadata and degree distributions]
- M Larsson, J Ugander
A Concave Regularization Technique for Sparse Mixture Models
Advances in Neural Information Processing Systems (NeurIPS) 24, 2011.
[NIPS poster]
- J Ugander, MJ Dunlop, RM Murray
Analysis of a Digital Clock for Molecular Computing
Proc. 2007 American Control Conference (ACC), New York, July 2007. p. 1595-1599.
Dormant:
Theses:
Teaching
- MS&E 135: Networks (Winter 2023)
Previous versions:
Winter 2022,
Winter 2021,
Winter 2020,
Winter 2019,
Spring 2018,
Winter 2017,
Spring 2016
This course provides an introduction to how networks underly our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include: an introduction to graph theory and graph concepts; social networks; information networks; the aggregate behavior of markets and crowds; network dynamics; information diffusion; the implications of popular concepts such as "six degrees of separation", the "friendship paradox", and the "wisdom of crowds".
- MS&E 231: Introdution to Computational Social Science (Fall 2022)
With a vast amount of data now collected on our online and offline actions – from what we buy, to where we travel, to who we interact with – we have an unprecedented opportunity to study traditional social systems with novel precision and detail, as well the emerging challenges of studying modern social systems infused with learning algorithms. In this hands-on course, we first develop ideas from computer science and statistics to address problems in sociology, economics, political science. Beyond that, we place a particular emphasis on the study of algorithm-in-the-loop social systems. To see how these techniques are applied in practice, we discuss recent research findings in a variety of areas. Prerequisites: introductory course in applied statistics and experience coding in Python.
- MS&E 234: Data Privacy and Ethics (Winter 2022)
Previous versions: Winter 2020, Winter 2019, Spring 2018
This course engages with ethical challenges in the modern practice of data science. The three main focuses are data privacy, personalization and targeting algorithms, and online experimentation. The focus on privacy raises both practical and theoretical considerations. As part of the module on experimentation, students are required to complete the Stanford IRB training for social and behavioral research. The course assumes a strong technical familiarity with the practice of machine learning and data science. Recommended: 221, 226, CS 161, or equivalents.
- MS&E 334: Topics in Social Data (Fall 2018)
Previous versions: Fall 2017, Fall 2016, Fall 2015
This course provides a in-depth survey of methods research for the analysis of large-scale social and behavioral data. There will be a particular focus on recent developments in discrete choice theory and preference learning. Connections will be made to graph-theoretic investigations common in the study of social networks. Topics will include random utility models, item-response theory, ranking and learning to rank, centrality and ranking on graphs, and random graphs. The course is intended for Ph.D. students, but masters students with an interested in research topics are welcome. Recommended: 221, 226, CS161, or equivalents.
One-off lecture notes:
Spectral theory for planar graphs, including the Spielman-Teng partitioning result. (9/29/2011)
Grant/project pages
Since joining the Stanford faculty my research has been generously supported by the National Science Foundation (NSF), the Army Research Office (ARO), Stanford's Cisco Systems (2022-25) and David Morgenthaler II Faculty Fellowships (2015), a Hellman Foundation Faculty Fellowship (2019), the Stanford Thailand Research Consortium, the Stanford Institute for Human-centered AI (HAI), the Stanford King Center on Global Development, the Stanford Program on Democracy and the Internet (PDI), the Koret Foundation, and Facebook. My Ph.D. students have individually received further external fellowship support from the NSF Graduate Research Fellowship and National Defense Science and Engineering Graduate (NDSEG) Fellowship programs.
A list of my federally funded grants:
- NSF CAREER Awarrd (2022-2027): Machine Learning with Behavioral and Social Data
- ARO MURI Award (2020-Present): A Multimodal Approach to Network Information Dynamics (co-PI)
- ARO Young Investigator Award (2019-2021): Models and Algorithms for Higher Order Network Inference
(Award #73348-NS-YIP)
- NSF CRII (2017-2019):
Algorithms for Causal Inference on Networks
(Award #1657104)
Activities
I have organized or co-chaired the following workshops:
I am serving/have served on the Program Committee
of the following conferences/workshops:
-
2023: GraphEx
-
2022: ACM KDD, SIAM NS, GraphEx, IC2S2
-
2021: ACM EC (area chair), NeurIPS (area chair), WWW, GraphEx, IC2S2
-
2020: ACM WSDM, WWW, GraphEx, IC2S2
-
2019: ACM WSDM, WWW, ACM EC (senior PC), ICCSS, SIAM NS, GraphEx
-
2018: ACM WSDM, WWW, ACM EC, SIAM NS (co-chair), Black in AI
-
2017: ACM WSDM, WWW (senior PC), NIPS (reviewer)
-
2016:
WWW, ACM EC, ICCSS, ACM KDD, SIAM NS, AAAI IJCAI, AAAI ICWSM (senior PC), SIAM SDM
-
2015: WWW, ACM EC, ICCSS, ACM KDD, SIAM SDM
-
2014: WWW, SocInfo, ACM CIKM, AAAI ICWSM
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2013: WebSci
I am also an Associate Editor for Science Advances (AAAS) (7/2022 - present), and increasingly find my reviewing time is devoted to journal review work.
Figure 2. The summit of Fairview Dome, Yosemite National Park, July 2012.
Selected press coverage
- Nature Research Communities, August 2019: Spotlight on Early-Career Researchers interview
- Scientific American, June 2018: Friends of Friends Can Reveal Hidden Information about a Person
- BBC Radio 4: Digital Human, May 2016: Lost and Found
- MIT Technology Review (blog), April 2015: Network Archaeologists Discover Two Types of Social Network Growth
- Wall Street Journal (blog), June 2014: Studying Your Users: Facebook's Greatest Hits
- Facebook Engineering Blog, April 2014: Large-scale graph partitioning with Apache Giraph
- Wired (blog), April 2013: Exploring the Space of Human Interaction
- SmartPlanet, October 2012: Q&A: Why you have fewer friends than your friends on Facebook
- NY Times Opinionator, September 2012: Friends You Can Count On
- Nature, August 2012: Computational Social Science: Making the Links
- American Mathematical Society, July 2012: SIAM Annual Meeting 2012 Highlights
- Science Now, April 2012: How Facebook "Contagion" Spreads
- New Scientist, April 2012: Variety, Not Viral Spread, is Key to Facebook Growth
- The Economist, April 2012: Social Contagion: Conflicting Ideas
- The Economist Daily Chart, March 2012: The Sun Never Sets
- The Telegraph, March 2012: Facebook: British Empire Still Shapes Friendship Patterns
- NPR (on-air interview), November 2011: 4.74 Degrees of Separation
- Wired, Novemeber 2011: Facebook Study: It's a Small(er) World After All
- TechCrunch, Novemeber 2011: 4.74 - Facebook Wins By Getting Us Closer Than Six Degrees
- NY Times, November 2011: Between You and Me? 4.74 Degrees
Bookmarklets
- scholarfy: a bookmarklet I wrote to transfer search queries to Google Scholar.
- JSTORpdf: a bookmarklet I wrote to access PDFs faster on JSTOR.
- googURL: a bookmarklet I wrote to circumvent some paywalls using Google.
Climbing
My wife and I spend a lot of our free time climbing. Sometimes we write trip reports.
First ascents:
Other trip reports:
I also enjoy trail running. Very occasionally I'll run competitively.
Misc
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common_crawl_stanford.edu_54
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Accepted papers and schedule now posted!
Overview
This workshop aims to bring together a diverse and cross-disciplinary set of researchers to discuss recent advances and future directions for developing new network methods in statistics and machine learning. In particular, we are interested in
- network methods that learn the patterns of interaction, flow of information, or propagation of effects in social and information systems,
- empirical studies, particularly attempts to bridge observational methods and causal inference, and studies that combine learning, networks, and computational social science,
- research that unifies the study of both structure and content in rich network datasets.
While this research field is broad and diverse, there are emerging signs of convergence. For example, in the study of information diffusion, social media and social network researchers are beginning to use rigorous tools to distinguish effects driven by social influence, homophily, or external processes -- subjects historically of intense interest amongst statisticians and social scientists. Similarly, there is a growing statistics literature developing learning approaches to study topics popularized earlier within the physics community, including clustering in graphs, network evolution, and random-graph models. Finally, learning methods are increasingly used in highly complex application domains, such as large-scale knowledge graph construction and use, and massive social networks like Facebook and LinkedIn. These applications are stimulating new scientific and practical questions that often cut across disciplinary boundaries.
Invited speakers:
Workshop schedule
Morning Session9:00 - 9:15 Opening Remarks
9:15 - 10:00 Invited talk, Jon Kleinberg, Cornell University
10:00 - 10:30 (Coffee break)
10:30 - 11:15 Poster spotlight session
11:15 - 12:30 Poster session
Lunch break (12:30-3:00pm)
Afternoon Session
3:00 - 3:45 Invited talk, Emily Fox, University of Washington
3:45 - 4:30 Invited talk, Hanna Wallach, Microsoft Research NYC
4:30 - 5:00 (Coffee break)
5:00 - 5:45 Invited talk, Deepak Agarwal, LinkedIn
5:45 - 6:00 Panel Discussion (with Deepak, Emily, Jon, and Hanna)
Poster presenters: please arrive 8:45-9:00 to set up your posters. Supplies for hanging posters will be provided.
Accepted papers
Bopeng Li, Sougata Chaudhuri, and Ambuj Tewari.Handling Class Imbalance in Link Prediction using Learning to Rank Techniques
Justin Khim and Po-Ling Loh.
Confidence Sets for the Source of a Diffusion in Regular Trees
Victor Veitch and Daniel Roy.
The Class of Random Graphs Arising from Exchangeable Random Measures
Shandian Zhe, Pengyuan Wang, Kuang-Chih Lee, Zenglin Xu, Jian Yang, Youngja Park, and Yuan Qi.
Distributed Flexible Nonlinear Tensor Factorization for Large Scale Multiway Data Analysis
Joshua Blumenstock, Gabriel Cadamuro, and Robert On.
Predicting Poverty and Wealth from Mobile Phone Metadata
Argyris Kalogeratos, Kevin Scaman, and Nicolas Vayatis.
Learning to Suppress SIS Epidemics in Networks
Elisabeth Baseman and David Jensen.
Collaborative Behavior in Social Networks: A Relational Statistical Approach
Varun Gangal, Abhishek Narwekar, Balaraman Ravindran, and Ramasuri Narayanam.
Trust And Distrust Across Coalitions - Shapley Value Centrality Measures For Signed Networks
Tanmay Sinha, Wenjun Wang, and Xuechen Lei.
Teasing Apart Behavioral Protocols in Longitudinal Self-reported Friendship Networks
Tanmay Sinha, Wei Wei, and Kathleen Carley.
Modeling Similarity in Incentivized Interaction: A Longitudinal Case Study of StackOverFlow
Yali Wan and Marina Meila.
Benchmarking recovery theorems for the DC-SBM
James Atwood and Don Towsley.
Search-Convolutional Neural Networks
Guilllermo Santamaría and Vicenç Gomez.
Convex inference for community discovery in signed networks
Aldo Porco, Andreas Kaltenbrunner, and Vicenç Gomez.
Low-rank approximations for predicting voting behaviour
Kevin Carter, Rajmonda Caceres, and Benjamin Priest.
Characterization of Latent Social Networks Discovered through Computer Network Logs
Lin Li and William Campbell.
Matching Community Structure Across Online Social Networks
Sucheta Soundarajan, Tina Eliassi-Rad, Brian Gallagher, and Ali Pinar.
MaxOutProbe: An Algorithm for Increasing the Size of Partially Observed Networks
Sharan Vaswani, Laks V.S. Lakshmanan, and Mark Schmidt.
Influence Maximization with Bandits
Konstantina Palla, Francois Caron, and Yee Whye Teh.
A Bayesian nonparametric model for sparse dynamic networks
Tamara Broderick and Diana Cai.
Edge-exchangeable graphs and sparsity
Pau Perng-Hwa Kung and Deb Roy.
Measuring Responsiveness in the Online Public Sphere for the 2016 U.S. Election
Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein.
Efficient Algorithms to Optimize Diffusion Processes under the Independent Cascade Model
Aaron Schein, Mingyuan Zhou, David Blei, and Hanna Wallach.
Modeling Topic-Partitioned Assortativity and Disassortativity in Dyadic Event Data (Best Student Poster Award)
Jack Hessel, Alexandra Schofield, Lillian Lee, and David Mimno.
What do Vegans do in their Spare Time? Latent Interest Detection in Multi-Community Networks
Diana Cai and Tamara Broderick.
Completely random measures for modeling power laws in sparse graphs
Yike Liu, Neil Shah, and Danai Koutra.
An Empirical Comparison of the Summarization Power of Graph Clustering Methods
Gintare Karolina Dziugaite, and Daniel Roy.
Neural Network Matrix Factorization
Chris Lloyd, Tom Gunter, Michael Osborne, and Stephen Roberts.
Inferring Dynamic Interaction Networks with N-LPPA
Charalampos Mavroforakis, Isabel Valera, and Manuel Gomez Rodriguez.
Hierarchical Dirichlet Hawkes Process for modeling the Dynamics of Online Learning Activity
Pinar Yanardag, and S.V.N. Vishwanathan.
A Submodular Framework for Graph Comparison
Online Submissions
We welcome the following types of papers:
- Research papers that introduce new models or apply established models to novel domains,
- Research papers that explore theoretical and computational issues, or
- Position papers that discuss shortcomings and desiderata of current approaches, or propose new directions for future research.
Submissions will be lightly peer-reviewed. We encourage authors to emphasize the role of learning and its relevance to the application domains at hand. In addition, we hope to identify current successes in the area, and will therefore consider papers that apply previously proposed models to novel domains and data sets.
Submission format
Submissions should be 4-to-8 pages long, and adhere to NIPS format (http://nips.cc/PaperInformation/StyleFiles). Please make the author information visible as in the final draft.
Submission procedure
Please contribute your submissions through https://easychair.org/conferences/?conf=nipsnetworks2015
Important dates
- Deadline for Submissions: Monday, November 2, 2015, 11:59pm PST
- Notification of Decision: Thursday, November 5, 2015
Organizers:
- Edo Airoldi, Harvard University
- David Choi, Carnegie Mellon University
- Aaron Clauset, University of Colorado, Boulder
- Panos Toulis, Harvard University
- Johan Ugander, Stanford University
Previous NIPS workshops:
- NIPS 2014: Networks: From Graphs to Rich Data
- NIPS 2013: Frontiers of Network Analysis: Methods, Models, and Applications
- NIPS 2012: Social Network and Social Media Analysis: Methods, Models and Applications
- NIPS 2010: Networks Across Disciplines: Theory and Applications
- NIPS 2009: Analyzing Networks and Learning with Graphs
Contact
You can reach the organizers at NIPSnetworks@gmail.com
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common_crawl_stanford.edu_55
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Judgment and Decision Making
Psych 154 (3 units, letter grade)
Bldg. 540, Rm. 108
M 3:00-5:50 PM
01/06/25-03/14/25
Prerequisites: Background in experimental psychology or economics.
Brian Knutson
Professor, Psychology & Neuroscience
Bldg 420, Room 476
Email: knutson'at'stanford.edu
Office Hours: W 2:00-3:30 PM
Appointments: https://calendly.com/knutson_brain/30min
Decisions pervade our existence and determine the course and quality of our lives, both as individuals and as a society. Despite their ubiquity, why are decisions so difficult and fallible, and what can we do to improve them?
This upper-division seminar explores decision-making from an interdisciplinary perspective, beginning with normative approaches (i.e., how should we choose?), followed by descriptive evidence (i.e., how do we choose?), and ending with prescriptive methods of closing the gap between them (i.e., how could we choose better?), as well applications.
Course goals include: (1) building familiarity with key concepts and theories related to decision making; (2) critical evaluation and constructive elaboration of relevant tools and evidence; (3) preparation to conduct innovative interdisciplinary research; and (4) application of findings to improve your own decision making (hopefully).
Kahneman, Daniel (2011). Thinking, fast and slow. New York: Farrar, Strauss, & Giroux, New York, NY.
The initial class touches on normative accounts of choice ("system 2") and tools. Subsequent classes will explore descriptive accounts of choice ("system 1+2") and relevant evidence.
Later classes will consider prescriptive accounts of choice ("system 3"), including strategies for optimizing choice.
The final class will feature students' presentations of proposed projects.
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common_crawl_stanford.edu_56
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Neuroforecasting
Psych 24N-01 (3 units, letter grade)
M 2:30-5:30 PM
01/06/20-03/13/20
Online (canvas.stanford.edu)
Prerequisites: First-year seminar (instructor consent).
Brian Knutson, PhD
Psychology & Neuroscience
Bldg 420, Room 470
Email: knutson'at'stanford.edu
https://stanford.edu/~knutson
Office Hours: W 2:00-3:30 PM (book through rschumm'at'stanford.edu)
This seminar explores whether brain activity can be used not only to predict the choices of individuals, but also to forecast the behavior of separate groups of individuals in the future (e.g., in markets). Questions include how neuroforecasting might be possible, whether it can add value to other forecasting tools (e.g., traditional measures like behavioral choice and subjective ratings), and when it might extend to different aggregate scenarios. The course is ideal for students with interdisciplinary interests who would like to extend neural predictions about individual choice to group choice, and who plan to apply this knowledge in their future research.
(readings are linked to the syllabus below)
Classes 1-2 provide minimal background in neuroscience and psychology.
Classes 3-7 include discussion of relevant research and findings.
Class 8 focuses on students' research presentations of proposed projects.
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common_crawl_stanford.edu_57
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Market Fragmentation and Inefficiencies in Maritime Shipping
Maritime transportation accounts for 90% of global trade, but ballasting—vessels traveling without cargo—imposes substantial economic and environmental costs. This paper examines the oil transportation industry, where approximately half of all miles traveled are sailed empty. While some ballasting is necessary due to inherent supply-demand imbalances in oil markets, our analysis demonstrates that market structure, specifically the fragmentation of vessel ownership, is also a primary driver, accounting for 10-20% of the total empty miles traveled depending on the market segment. In addition, we show that consolidating vessels into small shipping pools—sets of vessels operated under unified management—can reduce ballasting-related carbon emissions by up to 15%. This market-driven approach, which is gaining industry adoption, maintains competitive dynamics, given the limited scale of consolidation, while significantly improving efficiency. The gains arise from enhanced coordination within larger pools and expanded port coverage, reducing unnecessary vessel repositioning. More broadly, our findings quantitatively demonstrate that organizational changes alone—specifically, the consolidation of vessel operations—can generate significant environmental improvements by reducing empty miles. This provides a practical path toward sustainability that can complement and amplify the benefits of technological innovation.
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common_crawl_stanford.edu_58
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Download
Abstract:
Participants race towards completing an innovation project and learn about its feasibility from their own efforts and their competitors’ gradual progress. Information about the status of competition can alleviate some of the uncertainty inherent in the contest, but it can also adversely affect effort provision from the laggards. This paper explores the problem of designing the award structure of a contest and its information disclosure policy in a dynamic framework and provides a number of guidelines for maximizing the designer’s expected payoff. In particular, we show that the probability of obtaining the innovation as well as the time it takes to complete the project are largely affected by when and what information the designer chooses to disclose. Furthermore, we establish that intermediate awards may be used by the designer to appropriately disseminate information about the status of competition. Interestingly, our proposed design matches several features observed in real-world innovation contests.
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common_crawl_stanford.edu_59
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Download
Abstract:
This paper studies the strategic interaction between a monopolistic seller of an information product and a set of potential buyers that compete in a downstream market. The setting is motivated by information markets in which (i) sellers have the ability to offer information products of different qualities; and (ii) the information product provides potential buyers not only with more precise information about the fundamentals, but also with a coordination device that can be used in their strategic interactions with their competitors. Our results illustrate that the nature and intensity of competition among the information provider’s customers play first-order roles in determining her optimal strategy. We show that when the customers view their actions as strategic complements, the provider finds it optimal to offer the most accurate information at her disposal to all potential customers. In contrast, when buyers view their actions as strategic substitutes, the provider maximizes her profits by either (i) restricting the overall supply of the information product, or (ii) distorting its content by offering a product of inferior quality. We also establish that the provider’s incentive to restrict the supply or quality of information provided to the downstream market intensifies in the presence of information leakage.
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common_crawl_stanford.edu_60
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Download
Abstract:
Two-sided platforms play an important role in reducing frictions and facilitating trade, and in doing so they increasingly engage in collecting and processing data about supply and demand. This paper establishes that platforms have an incentive to strategically disclose (coarse) information about demand to the supply side as this can considerably boost their profits. However, this practice may also adversely affect the welfare of consumers. By optimally designing its information disclosure policy, a platform can influence the entry and pricing decisions of its potential suppliers. In general, it is optimal for the platform to disclose its information only partially to either nudge entry when it is a priori costly for suppliers to join or, conversely, discourage it when suppliers do not have access to attractive outside options. On the other hand, consumers may end up being worse off as they have access to fewer trading options and/or face higher prices compared to when the platform refrains from sharing any demand information to its potential suppliers.
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common_crawl_stanford.edu_61
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Download
Abstract:
Recent advances in information technology have allowed firms to gather vast amounts of data regarding consumers’ preferences and the structure and intensity of their social interactions. This paper examines a game-theoretic model of competition between firms which can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted advertising strategies and highlight their dependence on the underlying social network structure. Furthermore, we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals and establish a lower bound on the ratio of their payoffs in these asymmetric equilibria. Finally, we find that at equilibrium firms invest inefficiently high in targeted advertising and the extent of the inefficiency is increasing in the centralities of the agents they target. Taken together, these findings shed light on the effect of the network structure on the outcome of marketing competition between the firms.
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common_crawl_stanford.edu_62
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Dave Paunesku is a senior behavioral scientist at Stanford University and the founding director of PERTS, a nonprofit R&D institute. PERTS translates insights from the learning and developmental sciences into scalable tools, measures, and recommendations that educators use to cultivate student engagement and success equitably. Hundreds of thousands of educators and hundreds of schools and colleges use PERTS’ free, evidence-based resources.
Paunesku's work with academic, industry, and government partners has led to the development of evidence-based resources that have reached millions of learners worldwide.
His work has been published in leading academic journals, including Nature, Psychological Science, and Proceedings of the National Academy of Sciences. It has also been featured in The New York Times, Wall Street Journal, Education Week, Atlantic, USA Today, and The College Dropout Scandal.
PERTS projects have been funded by scientific and educational agencies and by private foundations, including the National Science Foundation, the Institute of Education Sciences, the U.S. Department of Education, the Raikes Foundation, Gates Foundation, Joyce Foundation, Overdeck Foundation, and Hewlett Foundation.
Paunesku serves on the advisory boards of Imagine and EL Education.
Paunesku grew up in Chicago before moving to the San Francisco Bay Area, where he co-founded PERTS with friends Carissa Romero, Ben Haley, and Chris Macrander. He earned a B.A. from the University of Chicago and a Ph.D. from Stanford University, where his dissertation was supervised by Professors Carol Dweck and Greg Walton. He lives in Oakland, California.
To request a workshop, presentation, or consulting engagement, email paunesku@gmail.com. For inquiries about PERTS, email contact@perts.net.
Paunesku, D. & Farrington, C.A. (2020). Measure Learning Environments, Not Just Students, to Support Learning and Development. Teachers College Record, 112.
link
Okonofua, J.A., Paunesku, D., & Walton, G.M. (2016). A Brief Intervention to Encourage Empathic Discipline Cuts Suspension Rates in Half Among Adolescents. Proceedings of the National Academy of Sciences.
link
Yeager, D. S., Walton, G. M., Brady, S. T., Akcinar, E. N., Paunesku, D., Keane, L., ... & Gomez, E. M. (2016). Teaching a lay theory before college narrows achievement gaps at scale. Proceedings of the National Academy of Sciences, 201524360. link
Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, 201608207.
link
Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E., Schneider, B., Hulleman, C.S., Hinojosa, C.P., Paunesku, D., Romero, C., Flint, K., Roberts, A., Trott, J., Iachan, R., Buontempo, J., Yang, S., Carvalho, C.M., Hahn, P.H., Gopalan, M., Mhatre, P., Ferguson, R., Duckworth, A.L., & Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364–369. link
Paunesku, D., Walton, G.M., Romero, C.L., Smith, E.N., Yeager, D.S., & Dweck, C.S. (2015). Mindset interventions are a scalable treatment for academic underachievement. Psychological Science, 26(6), 784-93.
link
Paunesku, D. (2013). Scaled-up social psychology: Intervening wisely and broadly in education. (Unpublished doctoral dissertation.) Stanford University. link
Yeager, D. S., Romero, C., Paunesku, D., Hulleman, C. S., Schneider, B., Hinojosa, C., ... & Trott, J. (2016). Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. Journal of Educational Psychology, 108(3), 374.
link
Yeager, D.S., Henderson, H., Paunesku, D., Walton, G.M., D’Mello, S. Spitzer, B.J., & Duckworth, A.L. (2014). Boring but Important: A self-transcendent purpose for learning fosters academic self-regulation. Journal of Personality and Social Psychology, 107(4), 559-580. link
Romero, C., Master, A., Paunesku, D., Dweck, C. S., & Gross, J. J. (2014). Academic and emotional functioning in middle school: The role of implicit theories. Emotion, 14(2), 227. link
Smith, E. N., Romero, C., Donovan, B., Herter, R., Paunesku, D., Cohen, G. L., . . . Gross, J. J. (2017). Emotion theories and adolescent well-being: Results of an online intervention. Emotion. Advance online publication. link
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common_crawl_stanford.edu_63
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At Stanford, I focus on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. I am CEO of Matroid and previously served on the Technical Advisory Board of Databricks.
Created CME 323: Distributed Algorithms and Optimization [2015] [2016] [2017] [2018] [2020] [2022] [2024]
Teaching CME 305: Discrete Mathematics and Algorithms [2014] [2015] [2016] [2017]
Won Best Paper Award runner-up at KDD 2016
Built the Scaled Machine Learning Conference
News in Bloomberg, Wall Street Journal, MIT Technology Review, TechCrunch, and many others
Interview: On the Evolution of Machine Learning, from Linear Models to Neural Networks [oreilly report]
Matroid was covered in The Wall Street Journal.
Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets [pubmed], TVST 2022
Detecting Glaucoma Using 3D Convolutional Neural Network of Raw SD-OCT Optic Nerve Scans [pdf], AAO 2019
FusionNet: 3D Object Classification Using Multiple Data Representations [pdf] [blog], NIPS 2016
Matrix Computations and Optimization in Apache Spark [pdf], KDD 2016
MLlib: Machine Learning in Apache Spark [arxiv], JMLR 2015
Dimension Independent Similarity Computation [pdf] [extension] [slides] [poster] [code] [press], JMLR 2014
Estimate of Shaking Intensity by Combining Earthquake Characteristics with Tweets [pdf] [slides] [video] [demo] [full], NCEE 2014
On the Precision of Social and Information Networks [pdf] [slides], COSN 2013
WTF: The Who to Follow Service at Twitter [pdf], WWW 2013
A Uniqueness Theorem for Clustering [pdf] [slides], UAI 2009
Address: Matroid HQ, Palo Alto
Email: hello@reza.ai
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common_crawl_stanford.edu_64
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At Stanford, I focus on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. I am CEO of Matroid and previously served on the Technical Advisory Board of Databricks.
Created CME 323: Distributed Algorithms and Optimization [2015] [2016] [2017] [2018] [2020] [2022] [2024]
Teaching CME 305: Discrete Mathematics and Algorithms [2014] [2015] [2016] [2017]
Won Best Paper Award runner-up at KDD 2016
Built the Scaled Machine Learning Conference
News in Bloomberg, Wall Street Journal, MIT Technology Review, TechCrunch, and many others
Interview: On the Evolution of Machine Learning, from Linear Models to Neural Networks [oreilly report]
Matroid was covered in The Wall Street Journal.
Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets [pubmed], TVST 2022
Detecting Glaucoma Using 3D Convolutional Neural Network of Raw SD-OCT Optic Nerve Scans [pdf], AAO 2019
FusionNet: 3D Object Classification Using Multiple Data Representations [pdf] [blog], NIPS 2016
Matrix Computations and Optimization in Apache Spark [pdf], KDD 2016
MLlib: Machine Learning in Apache Spark [arxiv], JMLR 2015
Dimension Independent Similarity Computation [pdf] [extension] [slides] [poster] [code] [press], JMLR 2014
Estimate of Shaking Intensity by Combining Earthquake Characteristics with Tweets [pdf] [slides] [video] [demo] [full], NCEE 2014
On the Precision of Social and Information Networks [pdf] [slides], COSN 2013
WTF: The Who to Follow Service at Twitter [pdf], WWW 2013
A Uniqueness Theorem for Clustering [pdf] [slides], UAI 2009
Address: Matroid HQ, Palo Alto
Email: hello@reza.ai
|
common_crawl_stanford.edu_65
|
At Stanford, I focus on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. I am CEO of Matroid and previously served on the Technical Advisory Board of Databricks.
Created CME 323: Distributed Algorithms and Optimization [2015] [2016] [2017] [2018] [2020] [2022] [2024]
Teaching CME 305: Discrete Mathematics and Algorithms [2014] [2015] [2016] [2017]
Won Best Paper Award runner-up at KDD 2016
Built the Scaled Machine Learning Conference
News in Bloomberg, Wall Street Journal, MIT Technology Review, TechCrunch, and many others
Interview: On the Evolution of Machine Learning, from Linear Models to Neural Networks [oreilly report]
Matroid was covered in The Wall Street Journal.
Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets [pubmed], TVST 2022
Detecting Glaucoma Using 3D Convolutional Neural Network of Raw SD-OCT Optic Nerve Scans [pdf], AAO 2019
FusionNet: 3D Object Classification Using Multiple Data Representations [pdf] [blog], NIPS 2016
Matrix Computations and Optimization in Apache Spark [pdf], KDD 2016
MLlib: Machine Learning in Apache Spark [arxiv], JMLR 2015
Dimension Independent Similarity Computation [pdf] [extension] [slides] [poster] [code] [press], JMLR 2014
Estimate of Shaking Intensity by Combining Earthquake Characteristics with Tweets [pdf] [slides] [video] [demo] [full], NCEE 2014
On the Precision of Social and Information Networks [pdf] [slides], COSN 2013
WTF: The Who to Follow Service at Twitter [pdf], WWW 2013
A Uniqueness Theorem for Clustering [pdf] [slides], UAI 2009
Address: Matroid HQ, Palo Alto
Email: hello@reza.ai
|
common_crawl_stanford.edu_66
|
Clustering is one of the most widely used techniques for exploratory data analysis. In the past five decades, many clustering algorithms have been developed and applied to a wide range of practical problems. There has also been very exciting theoretical work, proving guarantees for algorithms and developing new frameworks for analysis.
Yet in many ways we are only beginning to understand some of the most basic issues in clustering. While there have been some remarkable successes, we believe more is possible. In particular, work addressing issues that are independent of any specific clustering algorithm, objective function, or specific data generative model, is still in its infancy.
In his famous Turing award lecture, Donald Knuth states about Computer Programming that: "It is clearly an art, but many feel that a science is possible and desirable''. In the case of clustering, we believe that an even better and deeper science than what we currently offer is possible and highly desirable.
The workshop will also serve as a follow up meeting to the NIPS 2005 “Theoretical Foundations of clustering” workshop, a venue for the different research groups working on these issues to take stock, exchange view points and discuss the next challenges in this ambitious quest for theoretical foundations of clustering.
Text version of Call for Contributions
Morning session 7:30 - 8:15 Introduction - Presentations of different views on clustering by the workshop organizers.
8:15 - 9:15 Non-standard Approaches
9:15 - 9:30 Coffee Break
9:30 - 10:30 Evaluating clustering: the human factor and particular applications
10:30 - 11:00 Deepayan Chakrabarti (invited talk) - Clustering applications at Yahoo!
Afternoon session 3:30 - 4:30 Hierarchical Clustering
4:30 - 4:45 Coffee Break
4:45 - 5:45 Information Theoretic Approaches
5:45 - 6:30 Panel discussion
What is a Cluster? Perspectives from Game Theory [pdf] Marcello Pelillo
Clustering with Prior Information [pdf] Armen E. Allahverdyan, Aram Galstyan, Greg Ver Steeg
Finding a Better k: A psychophysical investigation of clustering [pdf] Joshua M. Lewis
Single Data, Multiple Clusterings [pdf] Sajib Dasgupta, Vincent Ng
An Empirical Study of Cluster Evaluation Metrics using Flow Cytometry Data [pdf] Nima Aghaeepour, Alireza Hadj Khodabakhshi, Ryan R. Brinkman
Some ideas for formalizing clustering schemes [pdf] Gunnar Carlsson, Facundo Memoli
A Characterization of Linkage-Based Clustering: An Extended Abstract [pdf] Margareta Ackerman, Shai Ben-David, David Loker
Information theoretic model selection in clustering [pdf] Joachim M. Buhmann
A PAC-Bayesian Approach to Formulation of Clustering Objectives [pdf] Yevgeny Seldin, Naftali Tishby
These papers will form a basis for discussion sessions during the workshop
Clustering: Science or Art? [pdf] Isabelle Guyon, Ulrike von Luxburg, Robert C. Williamson NIPS 2009 Workshop on Clustering Theory, Vancouver, Canada, December 2009
Thoughts on Clustering [pdf] Avrim Blum NIPS 2009 Workshop on Clustering Theory, Vancouver, Canada, December 2009
Avrim Blum is a professor of CS at Carnegie Mellon University.
Ulrike von Luxburg is a Senior Research Scientist at the Max Plank Institute in Tubingen, Germany.
Isabelle Guyon is an independent engineering consultant, working from California.
Reza Bosagh Zadeh is a graduate student at Carnegie Mellon University.
Margareta Ackerman is a graduate student at the University of Waterloo.
Robert C. Williamson is the Scientific Director of NICTA and a Professor in the Research School of Information Sciences and Engineering at the Australian National University.
This workshop is supported by the PASCAL Network of Excellence.
Email: nips09 at clusteringtheory.org
|
common_crawl_stanford.edu_67
|
Clustering is one of the most widely used techniques for exploratory data analysis. In the past five decades, many clustering algorithms have been developed and applied to a wide range of practical problems. There has also been very exciting theoretical work, proving guarantees for algorithms and developing new frameworks for analysis.
Yet in many ways we are only beginning to understand some of the most basic issues in clustering. While there have been some remarkable successes, we believe more is possible. In particular, work addressing issues that are independent of any specific clustering algorithm, objective function, or specific data generative model, is still in its infancy.
In his famous Turing award lecture, Donald Knuth states about Computer Programming that: "It is clearly an art, but many feel that a science is possible and desirable''. In the case of clustering, we believe that an even better and deeper science than what we currently offer is possible and highly desirable.
The workshop will also serve as a follow up meeting to the NIPS 2005 “Theoretical Foundations of clustering” workshop, a venue for the different research groups working on these issues to take stock, exchange view points and discuss the next challenges in this ambitious quest for theoretical foundations of clustering.
Text version of Call for Contributions
Morning session 7:30 - 8:15 Introduction - Presentations of different views on clustering by the workshop organizers.
8:15 - 9:15 Non-standard Approaches
9:15 - 9:30 Coffee Break
9:30 - 10:30 Evaluating clustering: the human factor and particular applications
10:30 - 11:00 Deepayan Chakrabarti (invited talk) - Clustering applications at Yahoo!
Afternoon session 3:30 - 4:30 Hierarchical Clustering
4:30 - 4:45 Coffee Break
4:45 - 5:45 Information Theoretic Approaches
5:45 - 6:30 Panel discussion
What is a Cluster? Perspectives from Game Theory [pdf] Marcello Pelillo
Clustering with Prior Information [pdf] Armen E. Allahverdyan, Aram Galstyan, Greg Ver Steeg
Finding a Better k: A psychophysical investigation of clustering [pdf] Joshua M. Lewis
Single Data, Multiple Clusterings [pdf] Sajib Dasgupta, Vincent Ng
An Empirical Study of Cluster Evaluation Metrics using Flow Cytometry Data [pdf] Nima Aghaeepour, Alireza Hadj Khodabakhshi, Ryan R. Brinkman
Some ideas for formalizing clustering schemes [pdf] Gunnar Carlsson, Facundo Memoli
A Characterization of Linkage-Based Clustering: An Extended Abstract [pdf] Margareta Ackerman, Shai Ben-David, David Loker
Information theoretic model selection in clustering [pdf] Joachim M. Buhmann
A PAC-Bayesian Approach to Formulation of Clustering Objectives [pdf] Yevgeny Seldin, Naftali Tishby
These papers will form a basis for discussion sessions during the workshop
Clustering: Science or Art? [pdf] Isabelle Guyon, Ulrike von Luxburg, Robert C. Williamson NIPS 2009 Workshop on Clustering Theory, Vancouver, Canada, December 2009
Thoughts on Clustering [pdf] Avrim Blum NIPS 2009 Workshop on Clustering Theory, Vancouver, Canada, December 2009
Avrim Blum is a professor of CS at Carnegie Mellon University.
Ulrike von Luxburg is a Senior Research Scientist at the Max Plank Institute in Tubingen, Germany.
Isabelle Guyon is an independent engineering consultant, working from California.
Reza Bosagh Zadeh is a graduate student at Carnegie Mellon University.
Margareta Ackerman is a graduate student at the University of Waterloo.
Robert C. Williamson is the Scientific Director of NICTA and a Professor in the Research School of Information Sciences and Engineering at the Australian National University.
This workshop is supported by the PASCAL Network of Excellence.
Email: nips09 at clusteringtheory.org
|
common_crawl_stanford.edu_68
|
Much of Machine Learning is based on Linear Algebra. Often, the prediction is a function of a dot product between the parameter vector and the feature vector. This essentially assumes some kind of independence between the features. In contrast, matrix parameters can be used to learn interrelations between features: The (i,j)th entry of the parameter matrix represents how feature i is related to feature j.
This richer modeling has become very popular. In some applications, like PCA and collaborative filtering, the explicit goal is inference of a matrix parameter. Yet in others, like direction learning and topic modeling, the matrix parameter instead pops up in the algorithms as the natural tool to represent uncertainty.
The emergence of large matrices in many applications has brought with it a slew of new algorithms and tools. Over the past few years, matrix analysis and numerical linear algebra on large matrices has become a thriving field. Also manipulating such large matrices makes it necessary to to think about computer systems issues.
This workshop aims to bring closer researchers in large scale machine learning and large scale numerical linear algebra to foster cross-talk between the two fields. The goal is to encourage machine learning researchers to work on numerical linear algebra problems, to inform machine learning researchers about new developments on large scale matrix analysis, and to identify unique challenges and opportunities. The workshop will conclude with a session of contributed posters.
This workshop will consist of invited talks and paper submissions for a poster session. The target audience of this workshop includes industry and academic researchers interested in machine learning, large distributed systems, numerical linear algebra, and related fields.
Nathan Srebro (TTI): An overview of matrix problems. PCA, CCA, PLS, matrix regularization, main update families [slides]
Satyen Kale (Yahoo Labs): Near-Optimal Algorithms for Online Matrix Prediction [slides]
Ben Recht (Wisconsin): Large scale matrix algorithms and matrix completion [slides]
David Woodruff (IBM): Sketching as a Tool for Numerical Linear Algebra [slides]
Yiannis Koutis (Puerto Rico-Rio Piedras): Spectral sparsification of graphs: an overview of theory and practical methods [slides]
Malik Magdon-Ismail (RPI): Efficiently implementing sparsity in learning [slides]
Ashish Goel (Stanford): Primal-Dual Graph Algorithms for MapReduce [slides]
Matei Zaharia (MIT): Large-scale matrix operations using a data-flow engine [slides]
Manfred Warmuth (UCSC): Large Scale Matrix Analysis and Inference [slides]
Linear Bandits, Matrix Completion, and Recommendation Systems [pdf]
Efficient coordinate-descent for orthogonal matrices through Givens rotations [pdf]
Improved Greedy Algorithms for Sparse Approximation of a Matrix in terms of Another Matrix [pdf]
Preconditioned Krylov solvers for kernel regression [pdf]
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms [pdf][supplementary]
Dimension Independent Matrix Square using MapReduce [pdf]
Email: nips13 at largematrix.org
|
common_crawl_stanford.edu_69
|
Much of Machine Learning is based on Linear Algebra. Often, the prediction is a function of a dot product between the parameter vector and the feature vector. This essentially assumes some kind of independence between the features. In contrast, matrix parameters can be used to learn interrelations between features: The (i,j)th entry of the parameter matrix represents how feature i is related to feature j.
This richer modeling has become very popular. In some applications, like PCA and collaborative filtering, the explicit goal is inference of a matrix parameter. Yet in others, like direction learning and topic modeling, the matrix parameter instead pops up in the algorithms as the natural tool to represent uncertainty.
The emergence of large matrices in many applications has brought with it a slew of new algorithms and tools. Over the past few years, matrix analysis and numerical linear algebra on large matrices has become a thriving field. Also manipulating such large matrices makes it necessary to to think about computer systems issues.
This workshop aims to bring closer researchers in large scale machine learning and large scale numerical linear algebra to foster cross-talk between the two fields. The goal is to encourage machine learning researchers to work on numerical linear algebra problems, to inform machine learning researchers about new developments on large scale matrix analysis, and to identify unique challenges and opportunities. The workshop will conclude with a session of contributed posters.
This workshop will consist of invited talks and paper submissions for a poster session. The target audience of this workshop includes industry and academic researchers interested in machine learning, large distributed systems, numerical linear algebra, and related fields.
Nathan Srebro (TTI): An overview of matrix problems. PCA, CCA, PLS, matrix regularization, main update families [slides]
Satyen Kale (Yahoo Labs): Near-Optimal Algorithms for Online Matrix Prediction [slides]
Ben Recht (Wisconsin): Large scale matrix algorithms and matrix completion [slides]
David Woodruff (IBM): Sketching as a Tool for Numerical Linear Algebra [slides]
Yiannis Koutis (Puerto Rico-Rio Piedras): Spectral sparsification of graphs: an overview of theory and practical methods [slides]
Malik Magdon-Ismail (RPI): Efficiently implementing sparsity in learning [slides]
Ashish Goel (Stanford): Primal-Dual Graph Algorithms for MapReduce [slides]
Matei Zaharia (MIT): Large-scale matrix operations using a data-flow engine [slides]
Manfred Warmuth (UCSC): Large Scale Matrix Analysis and Inference [slides]
Linear Bandits, Matrix Completion, and Recommendation Systems [pdf]
Efficient coordinate-descent for orthogonal matrices through Givens rotations [pdf]
Improved Greedy Algorithms for Sparse Approximation of a Matrix in terms of Another Matrix [pdf]
Preconditioned Krylov solvers for kernel regression [pdf]
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms [pdf][supplementary]
Dimension Independent Matrix Square using MapReduce [pdf]
Email: nips13 at largematrix.org
|
common_crawl_stanford.edu_70
|
Zetianyu Wang, Renzhi Jing, Sam Heft-Neal, Aaron Clark-Ginsberg, Debarati Guha-Sapir, Eran Bendavid, and Zachary Wagner. 2025. ``Impact of Tropical Cyclone Exposure on Child Mortality in Low and Middle Income Countries''. Science Advances, forthcoming.
Renzhi Jing, Sam Heft-Neal, Zetianyu Wang, Jie Chen, Minghao Qiu, Isaac Opper, Zachary Wagner, & Eran Bendavid. 2025. ``Decreased likelihood of schooling as a consequence of tropical cyclones: Evidence from 13 low- and middle-income countries''. PNAS, 122 (18): e2413962122.
[
paper]
Sam Heft-Neal, Martin Heger, Vaibhav Rathi & Marshall Burke. 2025. ``Identifying child growth effects of elevated pollution levels during pregnancy''. Environmental Research Letters, 20 (1).
[
paper]
[replication materials]
Youn Soo Jung, Mary Johnson, Marshall Burke, Sam Heft-Neal, Melissa Bondy, Sharon Chinthrajah, Mark Cullen, Lorene Nelson, Caleb Dresser, and Kari Nadeau. 2025. ``Fine Particulate Matter From 2020 California Wildfires and Mental Health–Related Emergency Department Visits''. JAMA Network Open, 8 (4)
[
paper]
Emma Krasovich Southworth, Minghao Qiu, Carlos Gould, Ayako Kawano, Jeff Wen, Sam Heft-Neal, Kara Voss, Alandra Lopez, Scott Fendorf, Jen Burney, & Marshall Burke. 2025. ``The chemical composition of wildfire smoke exposure in the United States''. Environmental Science & Technology.
[
paper]
[replication materials]
Agbessi Amouzou, Aluisio Barros, Jennifer Requejo, Cheikh Faye, Nadia Akseer, Eran Bendavid, Cauane Blumenberg, Josephine Borghi, Sama El Baz, Frederic Federspiel, Leonardo Ferreira, Elizabeth Hazel, Sam Heft-Neal, et al. 2025. ``Tracking progress in reproductive, maternal, newborn, child and adolescent health and nutrition: the Countdown to 2030 for Women’s, Children’s and Adolescents’ Health report''. The Lancet.
[
paper]
Minghao Qiu, Makoto Kelp, Sam Heft-Neal, Xiaomeng Jin, Carlos F. Gould, Daniel Tong, & Marshall Burke. 2024. ``Evaluating chemical transport and machine learning models for wildfire smoke PM2.5: Implications for assessment of health impacts''. Environmental Science & Technology.
[
paper]
Pascal Geldsetzer, Daniel Fridljand, Mathew Kiang, Eran Bendavid, Sam Heft-Neal, Marshall Burke, Alexander Thieme, & Tarik Benmarhnia. 2024. ``Sociodemographic and geographic variation in mortality attributable to air pollution in the United States''. Nature Medicine.
[
paper]
Patrick Behrer & Sam Heft-Neal. 2024. ``Higher air pollution in wealthy districts of most low- and middle-income countries''. Nature Sustainability, 7.
[paper]
[replication materials]
Jeff Wen, Sam Heft-Neal, Patrick Baylis, Judson Boomhower, & Marshall Burke. 2023. ``Quantifying fire-specific smoke exposure and health impacts''. PNAS, 120 (51).
[paper]
[replication materials]
Sam Heft-Neal, Carlos Gould, Marissa Childs, Mathew Kiang, Kari Nadeau, Mark Duggan, Eran Bendavid, & Marshall Burke. 2023. ``Emergency department visits respond non-linearly to wildfire smoke''. PNAS, 120 (39).
[paper]
[analysis code][
download paper figures]
[media coverage: (
Washington Post)]
Renzhi Jing, Sam Heft-Neal, Daniel Chavas, Max Griswold, Zetianyu Wang, Aaron Clark-Ginsberg, Debarati Guha-Sapir, Eran Bendavid, & Zachary Wagner. 2023. ``Global Population Profile of Tropical Cyclone Exposure from 2002 to 2019''. Nature, 624.
[paper]
[replication materials]
Carlos Gould, Sam Heft-Neal, Mary Prunicki, Juan Antonio Aguilera-Mendoza, Marshall Burke, & Kari Nadeau. 2023. ``Health effects of wildfires''. Annual Review of Medicine, 75.
[paper]
Jennifer Burney, Geeta Persad, Jonathan Proctor, Eran Bendavid, Marshall Burke, & Sam Heft-Neal. ``Geographically-resolved social cost of anthropogenic emissions accounting for both direct and climate-mediated effects''. 2022. Science Advances.
[paper] [
replication materials]
Marshall Burke, Sam Heft-Neal, Jessica Li, Anne Driscoll, Patrick Baylis, Matthieu Stigler, Joakim Weill, Jen Burney, Jeff Wen, Marissa Childs, & Carlos Gould. 2022. ``Exposures and behavioral responses to wildfire smoke''. Nature Human Behavior.
[paper]
[comment] [
replication materials] [
download paper figures][
5 min project overview talk][
cover photo]
[media coverage: (
SF Chronicle)
(
NBC Bay Area)
(
NBC Today in the Bay)
(
CBS)
(
The Guardian)
(
KCBS Radio)]
Jonas Miller, Emily Dennis, Sam Heft-Neal, Booil Jo, & Ian Gotlib. 2021. ``Fine Particulate Air Pollution, Early Life Stress, and their Interactive Effects on Adolescent Structural Brain Development: A Longitudinal Tensor-Based Morphometry Study''. Cerebral Cortex, 346.
[paper]
Marshall Burke, Anne Driscoll, Sam Heft-Neal, Jenny Xue, Jennifer Burney, & Michael Wara. 2021. “The changing risk and burden of wildfire in the US”. PNAS, 118 (2).
[paper] [
replication materials] [
download paper figures]
[media coverage: (
The Economist Daily Chart)
(
SF Chronicle)
(
CBS)
(
LA Times)
(
AP)
(
NYT)
(
Bloomberg)
(
KCRW)
(
KQED)]
Eran Bendavid, Ties Boerma, Nadia Akseer, Ana Langer, Espoir Bwenge Malembaka, Emelda A. Okiro, Paul Wise, Sam Heft-Neal, Robert E. Black, & Zulfiqar A. Bhutta. 2021. ``The effects of armed conflict on the health of women and children''. The Lancet, 397 (10273).
[paper]
Sam Heft-Neal, Jen Burney, Eran Bendavid, Kara Voss, & Marshall Burke. 2020. ``Dust pollution from the Sahara and African infant mortality''. Nature Sustainability, 3 (10).
[paper] [
replication materials] [
independent replication]
[
download paper figures] [
video][
cover photo]
[media coverage: (
VoxDev Podcast)(
Nat Geo)(
BBC Newsday Interview)(
Discover Magazine)]
Nathan Lo, Ribhav Gupta, David Addiss, Eran Bendavid, Sam Heft-Neal, Alexei Mikhailov, Antonio Montresor, & Pamela Sabina Mbabazi. 2020. ``Comparison of World Health Organization and Demographic and Health Survey data to estimate sub-national deworming coverage in pre-school children''. PLOS Neglected Tropical Diseases, 14 (8).
[paper]
Zachary Wagner, Sam Heft-Neal, Paul Wise, Robert Black, Marshall Burke, Ties Boerma, Zulfiqar Bhutta, & Eran Bendavid. 2019. ``Women and children living in areas of armed conflict in Africa: a geospatial analysis of mortality and orphanhood'' . The Lancet Global Health, 7 (12).
[
paper]
[
comment]
Nathan Lo, Sam Heft-Neal, Jean Coulibaly, Leslie Leonard, Eran Bendavid, & David Addiss. 2019. ``State of deworming coverage and equity in low-income and middle-income countries using household health surveys: a spatiotemporal cross-sectional study'' . The Lancet Global Health, 7 (11).
[
paper][
comment]
Corey Bradshaw, Sarah Otto, Alicia Annamalay, Sam Heft-Neal, Zach Wagner, & Peter Le Souef. 2019. ``Testing the socio-economic and environmental determinants of better child health outcomes in Africa: a cross-sectional study among nations''. BMJ Open, 9(9).
[
paper]
Sam Heft-Neal, Jennifer Burney, Eran Bendavid, & Marshall Burke. 2018. ``Robust relationship between air quality and infant mortality in Africa''. Nature, 559 (7713).
[
paper] [
replication materials][
download paper figures] [
video]
[
2019 CRF Top 10 Research Achievement Award ]
[media coverage: (
Nature Podcast Interview)
(
ABC)
(
CBS)
(
The Independent)
]
Zachary Wagner, Sam Heft-Neal, Zulfiqar Bhutta, Robert Black, Marshall Burke, & Eran Bendavid. 2018. ``Armed conflict and child mortality in Africa: a geospatial analysis''. The Lancet, 392 (10150).
[
paper]
[
comment 1]
[
comment 2]
[
comment 2 reply ]
[media coverage:
(
NPR)
(
U.S. News)
(
Reuters)
(
Xinhua)
]
Marshall Burke, Felipe Gonzalez, Patrick Baylis, Sam Heft-Neal, Ceren Baysan, Sanjay Basu, & Sol Hsiang. 2018. ``Effect of ambient temperature on suicide in the US and Mexico''. Nature Climate Change, 8 (1).
[
paper] [
comment]
[
comment reply]
[
replication materials][
download paper figures]
[
data visualization]
[media coverage:
(
The Atlantic)
(
Bloomberg)
(
CNN)
(
FT)
(
Fortune)
(
Reuters)
(
Scientific America)
(
SF Chronicle)
(
Time)
(
USA Today)
(
WEF)
]
Nathan Lo, Jedidiah Snyder, David Addiss, Sam Heft-Neal, Jason Andrews, & Eran Bendavid. 2018. ``Deworming in pre-school age children: A global empirical analysis of health outcomes''. PLOS Neglected Tropical Diseases, 12 (5).
[
paper]
Sam Heft-Neal, Marshall Burke, & David Lobell. 2017. ``Using remotely sensed surface temperature to estimate climate response functions''. Environmental Research Letters, 12 (1).
[
paper][
blog post]
Marshall Burke, Sam Heft-Neal, & Eran Bendavid. 2016. ``Understanding variation in child mortality across Sub-Saharan Africa: A spatial analysis''. The Lancet Global Health, 4 (12).
[
paper][
comment]
[
replication materials][
download paper figures][
download infant mortality data]
|
common_crawl_stanford.edu_71
|
Getting started with Python can be a little confusing, hopefully this webpage helps to get you going.
There are two components to running Python (locally).
First, you need Python itself, obviously. For both Mac and Windows, it's easiest to install Python using Anaconda.
Second, you need an editor to write your code. This is independent of Python, and in fact you can use your editor to write code in other languages as well.
Another option is to run Python in the cloud (see below)
In this clase, we will be using Python 2. The following two methods are preferred for installing Python.
Note that even though Macs come with Python, it is still recommended to install Python using either Anaconda or Homebrew.
One convenient method to set up your Python environment is using a free, pre-packaged distribution, such as Anaconda (Click the big bright blue button on the top right). This has the advantage that many relevant packages come pre-installed, possibly saving you some headaches later on. It also includes pip, the Python package manager.
Note that while Anaconda also comes with the Anaconda launcher and a bunch of other tools, you should not be using any of these for this course. We only use Anaconda for the convenience of installation of Python and the main packages, not the other stuff that comes with it as well.
If you want a bit more control over your Python distribution, then using Homebrew to install Python is useful. It also installs the package manager pip for you, which is very useful. However, it does not come with additional modules, such as numpy or scipy, though they can be installed easily using pip. A short tutorial can be found here.
To write code, you need an editor. While there are many options, old and new, Sublime Text is one you can't go wrong with. Note you can use it for free. A script is a simple file with text, such as
a = 'Hello,' b = 'world!' print a + ' ' + b
which you can save to your filesystem using the editor.
Now that you know how to write Python scripts, it's time to learn how to run them using Python.
Open the terminal, for example by searching for terminal using the spotlight search function.
To open the interpreter, simply enter python. To run a script, navigate to the folder that contains your script. Then simply run python script.py, given that script.py is the name of your script.
Online, you can find some good resources that introduce the terminal, such as
How to Use Terminal: The Basics
Navigating the Terminal: A Gentle Introduction
25 Terminal Tips Every Mac User Should Know
Getting started with Linux, Section 3 and beyond (Note that Linux and Mac have the same terminal)
Stanford course CS 1U 'Practical Unix’
Open the command prompt by searching for cmd in the Start menu. This is the Windows version of the terminal.
To open the interpreter, similar to the Mac terminal, we simply run python.
To run a script, navigate to the folder that contains your script, and then run python script.py, given that script.py is the file name.
Also for Windows, there are some good resources about the command prompt
Command Prompt Basics - A Getting Started Guide
Windows Command Prompt in 15 Minutes
To install new modules, make sure you have pip installed. It comes with both Anaconda and Homebrew, so if you have used either of them, you are good. Otherwise, it is also rather straightforward to install pip: See the documentation.
Then, open the terminal / command prompt, and run pip install module, where module is the name of the module. Note that you cannot run this from inside the Python interpreter.
When you have trouble getting Python up and running, or if you have Windows and would like to use a Unix environment, then there is an alternative: using your browser. There are several possibilities, but to be on the same page, I suggest you use Cloud 9. Cloud 9 will host your development environment on the cloud for free.
Please create an account at c9.io if you are not able to run Python locally, so you can at least get started.
|
common_crawl_stanford.edu_72
|
The project is meant for you to demonstrate the skills you have learnt by taking this class. You are free in the choice of your project. However, picking the right project can be hard, so it might a safe option to pick one of the project ideas listed below.
The goal of the project should be to have fun. You take this class because you want to learn about Python, and this is your opportunity to use Python in any way you like. The project should take you around 20 hours of work as a rough estimate.
Project proposal, due at the beginning of Lecture 4, on April 23. A brief write-up should describe what you intend to do. This document should be a pdf file, and should be about two paragraphs long.
Final version of Python scripts, due one week after Lecture 8, on May 28 at noon. The final version should include all source code and data, and be zipped. Make sure your code runs (whatever the input) smoothly.
Final write-up, due one week after Lecture 8, at noon An updated version of your write-up, explaining things that have changed and your results.
There are no late days and no exceptions. You are to work on the project on your own, though you are strongly encouraged to discuss your project and code with others.
Please turn in all your work using the Dropbox on Coursework.
Below you can find some project ideas, to give you a sense of what would be possible and the scope of the project. Some of the below might be a bit ambitious, but I think it is good to be ambitious and end up not implementing everything completely.
Of course, you are very welcome to do something very different. Let the following list then give you an idea of the scope of your project.
Recently, neural networks and deep learning have become very fashionable. You could either explore the existing packages and work on a dataset, or implement a neural network trainer yourself, which should include training and prediction. Of course your own implementation should not be as sophisticated as the ones in some of the packages, but it will give you a good understanding of neural networks.
References
A tutorial on deep learning
Blogpost about implementing a convolutional neural network
PyBrain, a neural network library for Python Also check out the source code on github.
The MovieLens 100k dataset holds information on a reasonably large set of movies and user ratings. Implement a recommendation system that predicts movie ratings for users, finds similar movies and users, etc.
Uber and Lyft are companies that connect cab drivers to customers. Implement a simulation system where drivers roam around in a world (a 2D grid) and customers pop up dynamically, are assigned to drivers, and then brought to their destination. Visualize the simulation, for example using Matplotlib.
Colege football stats has comprehensive datasets on the last 9 years of college football. There are plenty of ways to be creative, one option would be to find a new model to predict outcomes of games or rank teams. Start simple and improve your model as you add more of the data into your analysis.
The Enigma machine is a well known device to encrypt messages, primarily used by the Germans in the second world war and famously cracked by the Allied forces. It would be interesting to create a Python version of the Enigma machine, that can encrypt and decrypt messages. You can find plenty of information online about the Enigma machine. Of course, you could also implement more contemporary encryption methods.
Predator-prey models, such as the Lotka-Volterra equations, describe the dynamics of biological systems where two species interact. Using Python, one is able to simulate much more complicated biological systems, with multiple species and additional factors (e.g. location, proximity to water, etc.). Postulate the features of a dynamical system, implement a simulation and present results graphically.
The game 2048 is a simple yet addictive game. Write the game in Python, such that you can play it in your terminal. Additionaly, write a computer agent that plays the game automatically and does reasonably well by coming up with some clever heuristics.
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线性代数和微积分回顾
原创内容 Afshine Amidi 和 Shervine Amidi
翻译: 朱小虎
通用符号
定义
向量 我们记 为一个 $n$ 维的向量，其中 $x_i\in\mathbb{R}$ 是第 $i$ 维的元素：
矩阵 我们记 $A\in\mathbb{R}^{m\times n}$ 为一个 $m$ 行 $n$ 列的矩阵，其中 $A_{i,j}\in\mathbb{R}$ 是第 $i$ 行 $j$ 列的元素：
注意：如上定义的向量 $x$ 可以被看做是一个 $n\times1$ 的矩阵，常被称为一个列向量。
主要的矩阵
单位矩阵 单位矩阵 $I\in\mathbb{R}^{n\times n}$ 是一个方阵其对角线上均是 1 其余位置均为 0。
注：对所有矩阵 $A\in\mathbb{R}^{n\times n}$，我们有 $A\times I=I\times A=A$。
对角阵 对角阵 $D\in\mathbb{R}^{n\times n}$ 是一个方阵其对角线上元素均是非零值，其余位置均为 0。
注：我们记 $D$ 为 $\textrm{diag}(d_1,...,d_n)$。
矩阵运算
乘法
向量-向量 存在两种类型的向量-向量乘积：
- 内积：对 $x,y\in\mathbb{R}^n$，我们有：
- 外积： 对 $x\in\mathbb{R}^m, y\in\mathbb{R}^n$，我们有：
矩阵-向量 矩阵 $A\in\mathbb{R}^{m\times n}$ 和向量 $x\in\mathbb{R}^{n}$ 的乘积是一个大小为 $\mathbb{R}^{m}$ 的向量，满足：
矩阵-矩阵 矩阵 $A\in\mathbb{R}^{m\times n}$ 和 $B\in\mathbb{R}^{n\times p}$ 的乘积是一个大小为 $\mathbb{R}^{m\times p}$ 的矩阵，满足：
其他操作
转置 矩阵 $A\in\mathbb{R}^{m\times n}$ 的转置，记作 $A^T$， 是其中元素的翻转
注：对矩阵 $A,B$，我们有 $(AB)^T=B^TA^T$
逆 可逆方阵 $A$ 的逆记作 $A^{−1}$ 和唯一满足下列要求的矩阵：
注：不是所有方阵都是可逆的。同样，对矩阵 $A,B$，我们有 $(AB)^{-1}=B^{-1}A^{-1}$
迹 方阵 $A$ 的迹，记作 $\textrm{tr}(A)$，是对角线元素的和：
注：对矩阵 $A,B$，我们有 $\textrm{tr}(A^T)=\textrm{tr}(A)$ 和 $\textrm{tr}(AB)=\textrm{tr}(BA)$
行列式 方阵 的行列式，记作 $|A|$ 或者 $\textrm{det}(A)$ 采用去掉第 $i$ 行 $j$ 列的矩阵 $A_{\backslash i, \backslash j}$ 递归表达为如下形式：
注：$A$ 可逆当且仅当 $|A|\neq0$。同样，有 $|AB|=|A||B|$ 和 $|A^T|=|A|$。
矩阵的性质
定义
对称分解 一个给定矩阵 $A$ 可以用其对阵和反对称部分进行表示：
范数 一个范数是一个函数 $N:V\longrightarrow[0,+\infty[$ 其中 $V$ 是一个向量空间，满足对所有 $x,y\in V$，有：
- $N(x+y)\leqslant N(x)+N(y)$
- 对一个标量 $a$，有 $N(ax)=|a|N(x)$
- 若 $N(x)=0$，则 $x=0$
对 $x∈V$，最常用的范数列在下表中：
线性相关 向量集合被称作线性相关的当其中一个向量可以被定义为其他向量的线性组合。
注：若无向量可以按照此法表示，则这些向量被称为线性无关。
矩阵的秩 给定矩阵 $A$ 的秩记作 $\textrm{rank}(A)$ 是由列向量生成的向量空间的维度。这等价于 $A$ 的线性无关列向量的最大数目。
半正定矩阵 矩阵 $A\in\mathbb{R}^{n\times n}$ 是半正定矩阵（PSD），记作 $A\succeq 0$，当我们有：
注：类似地，矩阵 $A$ 被称作正定，记作 $A\succ0$，当它是一个 PSD 矩阵且满足所有非零向量 $x$，$x^TAx>0$。
特征值，特征向量 给定矩阵 $A\in\mathbb{R}^{n\times n}$，$\lambda$ 被称作 $A$ 的一个特征值当存在一个向量 $z\in\mathbb{R}^n\backslash\{0\}$ 称作特征向量，满足：
谱定理 令 $A\in\mathbb{R}^{n\times n}$，若 $A$ 是对称的，则 $A$ 可以被一个实正交矩阵 $U\in\mathbb{R}^{n\times n}$ 对角化。记 $\Lambda=\textrm{diag}(\lambda_1,...,\lambda_n)$，我们有：
奇异值分解 对一个给定矩阵 $A$，其维度为 $m\times n$，奇异值分解（SVD）是一个因子分解机巧，能保证存在酉矩阵 $U$ $m\times m$，对角阵 $\Sigma$ $m\times n$ 和酉矩阵 $V$ $n\times n$，满足：
矩阵的微积分
梯度 令 $f:\mathbb{R}^{m\times n}\rightarrow\mathbb{R}$ 一个函数 $A\in\mathbb{R}^{m\times n}$ 一个矩阵。$f$ 关于 $A$ 的梯度是一个 mxn 的矩阵，记作 $\nabla_A f(A)$，满足：
注：$f$ 的梯度仅当 $f$ 是返回一个标量的函数时有定义。
Hessian 令 $f:\mathbb{R}^{n}\rightarrow\mathbb{R}$ 一个函数，$x\in\mathbb{R}^{n}$ 一个向量。$f$ 的关于 $x$ 的 Hessian 是一个 $n\times n$ 的对称阵，记作 $\nabla_x^2 f(x)$，满足：
注：$f$ 的 Hessian 仅当 $f$ 是一个返回标量的函数时有定义。
梯度运算 对矩阵 $A,B,C$，下列梯度性质值得记住：
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Recurrent Neural Networks cheatsheet
By Afshine Amidi and Shervine Amidi
Overview
Architecture of a traditional RNN Recurrent neural networks, also known as RNNs, are a class of neural networks that allow previous outputs to be used as inputs while having hidden states. They are typically as follows:
For each timestep $t$, the activation $a^{< t >}$ and the output $y^{< t >}$ are expressed as follows:
The pros and cons of a typical RNN architecture are summed up in the table below:
Applications of RNNs RNN models are mostly used in the fields of natural language processing and speech recognition. The different applications are summed up in the table below:
Loss function In the case of a recurrent neural network, the loss function $\mathcal{L}$ of all time steps is defined based on the loss at every time step as follows:
Backpropagation through time Backpropagation is done at each point in time. At timestep $T$, the derivative of the loss $\mathcal{L}$ with respect to weight matrix $W$ is expressed as follows:
Handling long term dependencies
Commonly used activation functions The most common activation functions used in RNN modules are described below:
Vanishing/exploding gradient The vanishing and exploding gradient phenomena are often encountered in the context of RNNs. The reason why they happen is that it is difficult to capture long term dependencies because of multiplicative gradient that can be exponentially decreasing/increasing with respect to the number of layers.
Gradient clipping It is a technique used to cope with the exploding gradient problem sometimes encountered when performing backpropagation. By capping the maximum value for the gradient, this phenomenon is controlled in practice.
Types of gates In order to remedy the vanishing gradient problem, specific gates are used in some types of RNNs and usually have a well-defined purpose. They are usually noted $\Gamma$ and are equal to:
where $W, U, b$ are coefficients specific to the gate and $\sigma$ is the sigmoid function. The main ones are summed up in the table below:
GRU/LSTM Gated Recurrent Unit (GRU) and Long Short-Term Memory units (LSTM) deal with the vanishing gradient problem encountered by traditional RNNs, with LSTM being a generalization of GRU. Below is a table summing up the characterizing equations of each architecture:
Remark: the sign $\star$ denotes the element-wise multiplication between two vectors.
Variants of RNNs The table below sums up the other commonly used RNN architectures:
Learning word representation
In this section, we note $V$ the vocabulary and $|V|$ its size.
Motivation and notations
Representation techniques The two main ways of representing words are summed up in the table below:
Embedding matrix For a given word $w$, the embedding matrix $E$ is a matrix that maps its 1-hot representation $o_w$ to its embedding $e_w$ as follows:
Remark: learning the embedding matrix can be done using target/context likelihood models.
Word embeddings
Word2vec Word2vec is a framework aimed at learning word embeddings by estimating the likelihood that a given word is surrounded by other words. Popular models include skip-gram, negative sampling and CBOW.
Skip-gram The skip-gram word2vec model is a supervised learning task that learns word embeddings by assessing the likelihood of any given target word $t$ happening with a context word $c$. By noting $\theta_t$ a parameter associated with $t$, the probability $P(t|c)$ is given by:
Remark: summing over the whole vocabulary in the denominator of the softmax part makes this model computationally expensive. CBOW is another word2vec model using the surrounding words to predict a given word.
Negative sampling It is a set of binary classifiers using logistic regressions that aim at assessing how a given context and a given target words are likely to appear simultaneously, with the models being trained on sets of $k$ negative examples and 1 positive example. Given a context word $c$ and a target word $t$, the prediction is expressed by:
Remark: this method is less computationally expensive than the skip-gram model.
GloVe The GloVe model, short for global vectors for word representation, is a word embedding technique that uses a co-occurence matrix $X$ where each $X_{i,j}$ denotes the number of times that a target $i$ occurred with a context $j$. Its cost function $J$ is as follows:
where $f$ is a weighting function such that $X_{i,j}=0\Longrightarrow f(X_{i,j})=0$.
Given the symmetry that $e$ and $\theta$ play in this model, the final word embedding $e_w^{(\textrm{final})}$ is given by:
Remark: the individual components of the learned word embeddings are not necessarily interpretable.
Comparing words
Cosine similarity The cosine similarity between words $w_1$ and $w_2$ is expressed as follows:
Remark: $\theta$ is the angle between words $w_1$ and $w_2$.
$t$-SNE $t$-SNE ($t$-distributed Stochastic Neighbor Embedding) is a technique aimed at reducing high-dimensional embeddings into a lower dimensional space. In practice, it is commonly used to visualize word vectors in the 2D space.
Language model
Overview A language model aims at estimating the probability of a sentence $P(y)$.
$n$-gram model This model is a naive approach aiming at quantifying the probability that an expression appears in a corpus by counting its number of appearance in the training data.
Perplexity Language models are commonly assessed using the perplexity metric, also known as PP, which can be interpreted as the inverse probability of the dataset normalized by the number of words $T$. The perplexity is such that the lower, the better and is defined as follows:
Remark: PP is commonly used in $t$-SNE.
Machine translation
Overview A machine translation model is similar to a language model except it has an encoder network placed before. For this reason, it is sometimes referred as a conditional language model.
The goal is to find a sentence $y$ such that:
Beam search It is a heuristic search algorithm used in machine translation and speech recognition to find the likeliest sentence $y$ given an input $x$.
• Step 1: Find top $B$ likely words $y^{< 1 >}$
• Step 2: Compute conditional probabilities $y^{< k >}|x,y^{< 1 >},...,y^{< k-1 >}$
• Step 3: Keep top $B$ combinations $x,y^{< 1>},...,y^{< k >}$
Remark: if the beam width is set to 1, then this is equivalent to a naive greedy search.
Beam width The beam width $B$ is a parameter for beam search. Large values of $B$ yield to better result but with slower performance and increased memory. Small values of $B$ lead to worse results but is less computationally intensive. A standard value for $B$ is around 10.
Length normalization In order to improve numerical stability, beam search is usually applied on the following normalized objective, often called the normalized log-likelihood objective, defined as:
Remark: the parameter $\alpha$ can be seen as a softener, and its value is usually between 0.5 and 1.
Error analysis When obtaining a predicted translation $\widehat{y}$ that is bad, one can wonder why we did not get a good translation $y^*$ by performing the following error analysis:
Bleu score The bilingual evaluation understudy (bleu) score quantifies how good a machine translation is by computing a similarity score based on $n$-gram precision. It is defined as follows:
Remark: a brevity penalty may be applied to short predicted translations to prevent an artificially inflated bleu score.
Attention
Attention model This model allows an RNN to pay attention to specific parts of the input that is considered as being important, which improves the performance of the resulting model in practice. By noting $\alpha^{< t, t'>}$ the amount of attention that the output $y^{< t >}$ should pay to the activation $a^{< t' >}$ and $c^{< t >}$ the context at time $t$, we have:
Remark: the attention scores are commonly used in image captioning and machine translation.
Attention weight The amount of attention that the output $y^{< t >}$ should pay to the activation $a^{< t' >}$ is given by $\alpha^{< t,t' >}$ computed as follows:
Remark: computation complexity is quadratic with respect to $T_x$.
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Johannes Voss is staff scientist at
SLAC National Accelerator Laboratory.
He leads the data science and electronic structure method efforts at the SUNCAT Center for Interface Science and Catalysis. The team develops machine learning models for the accurate and efficient prediction of catalytic reaction energies and materials properties. These developments include exchange-correlation functionals for computational surface science and efficient beyond density functional theory approaches. The team uses machine learning and first-principles methods to gain an understanding of energy and charge transfer and reaction mechanisms governing the performance of catalysts and battery materials.
Within the Ultrafast Catalysis FWP at SLAC, he leads the efforts of X-ray spectra simulations for understanding of ultrafast surface chemistry as observed using free-electron lasers.
PhD in Physics
Technical University of Denmark
Diploma (MSc) in Physics
University of Hamburg
Simulating and understanding charge transfer and ultrashort-lived excitations in surface femtochemistry.
Development of exchange-correlation functionals with improved description of bulk thermodynamics and surface reaction energetics.
Modeling of solid-state electrolyte and inter-phase stabilities and charge double layers at electrode interfaces for potential all-solid-state Li-ion batteries.
Method development and computational search for new perovskite structure light absorbers for use in solar cells or as water-splitting photocatalysts.
Ab initio-based design of new stable materials with high thermionic emission currents for use as energy converters or cathodes.
Simulation of ionic diffusion in energy storage materials and efficient calculation of phonon free energies for materials stability predictions.
Johannes Voss is regular guest lecturer at Stanford in electronic structure and heterogeneous catalysis classes (CHEMENG-444-01/ENERGY-256-01 and CHEMENG 142/242).
Topics covered include the basics of density functional theory & beyond and how this method can be applied to predict reaction rates, thermodynamic stabilities, and other materials properties.
For information on the accompanying exercises see the TA-maintained website http://chemeng444.github.io (initiated by Charlie Tsai).
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Johannes Voss is staff scientist at
SLAC National Accelerator Laboratory.
He leads the data science and electronic structure method efforts at the SUNCAT Center for Interface Science and Catalysis. The team develops machine learning models for the accurate and efficient prediction of catalytic reaction energies and materials properties. These developments include exchange-correlation functionals for computational surface science and efficient beyond density functional theory approaches. The team uses machine learning and first-principles methods to gain an understanding of energy and charge transfer and reaction mechanisms governing the performance of catalysts and battery materials.
Within the Ultrafast Catalysis FWP at SLAC, he leads the efforts of X-ray spectra simulations for understanding of ultrafast surface chemistry as observed using free-electron lasers.
PhD in Physics
Technical University of Denmark
Diploma (MSc) in Physics
University of Hamburg
Simulating and understanding charge transfer and ultrashort-lived excitations in surface femtochemistry.
Development of exchange-correlation functionals with improved description of bulk thermodynamics and surface reaction energetics.
Modeling of solid-state electrolyte and inter-phase stabilities and charge double layers at electrode interfaces for potential all-solid-state Li-ion batteries.
Method development and computational search for new perovskite structure light absorbers for use in solar cells or as water-splitting photocatalysts.
Ab initio-based design of new stable materials with high thermionic emission currents for use as energy converters or cathodes.
Simulation of ionic diffusion in energy storage materials and efficient calculation of phonon free energies for materials stability predictions.
Johannes Voss is regular guest lecturer at Stanford in electronic structure and heterogeneous catalysis classes (CHEMENG-444-01/ENERGY-256-01 and CHEMENG 142/242).
Topics covered include the basics of density functional theory & beyond and how this method can be applied to predict reaction rates, thermodynamic stabilities, and other materials properties.
For information on the accompanying exercises see the TA-maintained website http://chemeng444.github.io (initiated by Charlie Tsai).
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All-solid-state batteries have the potential to become high energy density and safe replacements for the commonly used liquid electrolyte Li-ion batteries. The considered solid electrolytes are not flammable in constrast to the liquids thus eliminating the security risks due to e.g. battery fires. Some solid electrolytes are electrochemically stable against lithium metal, opening up the possibility to replace graphitic intercalation anodes with lithium metal anodes, which would increase energy density.
Unlike the electrolytes used in solid oxide fuel cells, which require high temperatures for good conductivity, there are several so-called super-ionic solid Li-ion electrolytes, that have at least as high conductivities as the commonly used liquid electrolyte at moderate temperatures (with even better temperature ranges). However, only solid-state microbatteries are currently of practical use; solid-state batteries with high power density and long cycle life that could be used in electric vehicles have remained elusive. Given the good bulk conductivities of the electrolytes, interfacial issues between electrolyte and electrodes clearly play a role in the power density limitations of all-solid-state batteries [Luntz, Voss, Reuter, J. Phys. Chem. Lett. 6, 4599 (2015)].
Ideal Solid-solid Electrochemical Interfaces
We consider sharp, ideal interfaces between the solid electrolyte and the electrodes. We neglect mechanical contact problems (that likely will occur during charge/discharge cycles) or complex interface morphology.
For the above figure, we have used a continuum modeling approach, where the discrete structure of the charge density with lattice sites is neglected and charge and potential are considered homogeneous parallel to the interface. The problem is thus reduced to one dimension. Anode and cathode simply are modeled as metallic electrostatic boundary conditions.
Density functional theory calculations can provide important parameters for modeling the electrolyte here: dielectric constant and the formation energies of charge-carrying Li defects. These formation energies govern what potential energy the carriers have in the bulk of the electrolyte (i.e. in sufficient distance to the electrodes). For electrolytes such as lithium lanthanum zirconate or lithium oxychloride, the typical formation energies of Li+ vacancies is about 1-1.5eV relative to Li metal. Most of the desirable open circuit battery voltage of 4-5eV will thus drop at the interface to the cathode (see figure above).
DFT Simulations of Ideal Solid-electrolyte Interfaces
The above continuum simulations with parameters typical for solid-state batteries (dielectric constant ~15, low carrier concentrations) suggest very thin charge double layers. We have performed density functional theory (DFT) simulations to investigate the double charge layers in more detail [Stegmaier, Voss, Reuter, Luntz, Chem. Mater. 29, 4330 (2017)]. The DFT calculations indeed indicate even thinner double layers consisting almost only out of the Helmholtz plane (the layer of charges closest to interface) and vanishing diffuse layer.
Rather than explicitly modeling an electrode|electrolyte interface, we only model the electrolyte quantum-mechanically within DFT (we have chosen Li3OCl as a representative case for a super-ionic electrolyte here). The electrode is modeled using a polarizable medium with very high dielectric constant (see figure above), so that electric fields are screened with image charges behaving similarly to an ideal metallic interface. We find that ionic relaxation screens the interaction between charge carriers so efficiently, that basically a whole plane of Li could be depleted. The strong image stabilization of carriers at the electrode interface outweighs vanishingly small carrier interactions. The limitation to carrier accumulation in the Helmholtz plane is entropy, which only becomes important here at carrier coverages approaching unity (see figure below).
The simulations with the polarizable medium furthermore show that a depleted Helmholtz plane in Li3OCl could easily screen several Volts of potential drop (a result we expect to be generally true for similar solid ionic electrolytes). Since charge carrier interactions are limited to a single lattice spacing range due to effective ionic relaxation, accumulating significant amounts of charge in the Helmholtz plane is energetically feasible. We thus expect very thin charge double layers in solid-state batteries with strongly ionic electrolytes. Similar arguments hold for accumulation of positive charge (locally/intrinsically generated by strong negative potentials even in the case of vacancy mediated ion conduction) at the anode [Stegmaier, Voss, Reuter, Luntz, Chem. Mater. 29, 4330 (2017)].
Realistic interfaces will not be sharp, which can have an important effect on the double layers. More importantly, mechanical problems of electrolyte cracking by growing Li metal and contact problems pose considerable obstacles towards devising all-solid-state batteries with long cycle lifes and high power densities.
Ionic Diffusion in Glassy Solid Electrolytes
Modeling of ionic diffusion in glassy electrolytes and glassy systems in general requires sufficient statistical sampling using sufficiently large supercells, which generally renders first-principles approaches unfeasible. Only consisting of low atomic number elements, Li3OCl lends itself to be studied with force fields, enabling access to sufficiently long timescales.
Interestingly, using such force field molecular dynamics, we find that not only Li+ ions are mobile, but also to some extent Cl-. The picture of a solid-state electrolyte as a single-ion conductor with only one mobile species is thus too simple here. Similar (unwanted) counter-ion mobility could also exist in other ionic electrolytes.
Parasitic Electronic Conductivity in Solid Electrolytes
Another type of unwanted charge mobility in electrolytes is electronic conductivity. Electrons could, e.g., interact with Li ions to form neutral, metallic Li, which can lead to catastrophic battery failure due to a short. We simulated electron transport in two prototype solid-state electrolytes: cubic Li7La3Zr2O12 (c-LLZO) and Li7P3S11 (LPS). We find that electron transport in these electrolytes would be facilitated by polaron hopping (see figure below for a visualization of the corresponding charge localization), where the polaron concentration will strongly depend on synthesis conditions [Demir, Tekin, Chan, Scheurer, Reuter, Luntz, Voss, ACS Appl. Energy Mater. 7, 2392 (2024)].
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Hi! I'm Dan Yamins. Welcome to my website! I'm a computer scientist and computational cognitive neuroscientist at Stanford University, where I'm an associate professor of Psychology and Computer Science, and a faculty scholar at the Wu Tsai Neurosciences Institute. I work on science and technology challenges at the intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis.
The brain is the embodiment of the most beautiful algorithms ever written. My research group, the Stanford NeuroAILab, seeks to "reverse engineer" these algorithms, both to learn both about how our minds work and build more effective artificial intelligence systems.
I also like: (a) bonsai trees, (b) playing the pipe organ, (c) traveling in Asia, and (d) history.
The brain is the embodiment of the most beautiful algorithms ever written. My research group, the Stanford NeuroAILab, seeks to "reverse engineer" these algorithms, both to learn both about how our minds work and build more effective artificial intelligence systems.
I also like: (a) bonsai trees, (b) playing the pipe organ, (c) traveling in Asia, and (d) history.
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Yinyu Ye
K. T. Li Professor of Engineering
Management Science & Engineering and Institute of Computational & Mathematical Engineering
Huang Engineering Center 308
475 Via Ortega
School of Engineering
Stanford University, CA 94305-4121
Phone: 650 723-7262
Fax: 650 723-1614
Education
Ph.D. Engineering Economic Systems and Operations Research , Stanford University
, 1988.
M.S. Engineering Economic Systems , Stanford University
, 1983.
B.S. Systems and Control,
Huazhong University of Science and Technology , Wuhan, China, 1982.
Research Interest
Linear and Mathematical Programming
Optimization Algorithm Design and Analysis
Operations Research Models and their Applications
Algorithmic Game and Computational Complexity
My Google Scholar Link
Here are Courses I am teaching at Stanford for 2023-2024
Here are some Talks I made most recently
The fifth Edition of BOOK Linear and Nonlinear Programming by David G. Luenberger and Yinyu Ye has been published.
Click here for information .
Also see Table of Contents , Lecture Slides in various courses I am teaching, Chapter-by-Chapter Matlab Demonstration Codes/Scripts , and 5thErrata ; Solution Manual is also avaiable for intructors upon request.
The BOOK Interior-Point Algorithms: Theory and Analysis has been published.
Click here for information and related software .
Yinyu Ye
K.T. Li Professor of Engineering
Department of Management Science and Engineering
School of Engineering
Stanford University
Stanford, CA 94305
email: yinyu-ye@stanford.edu
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Stanford’s interactive Biology Cloud Lab hosts remote-controlled experiments over the Internet
Learning to do science involves more than reading books or peering through microscopes. It means learning how to make and test hypotheses, perform experiments and collect and analyze data.
To help keep cost and logistics from barring students from meaningful science education, Stanford researchers have designed a biology lab that is accessible over the Internet and extremely affordable at scale.
The Biology Cloud Lab is designed by a team led by Ingmar Riedel-Kruse, an assistant professor of bioengineering at Stanford, and Paulo Blikstein, a professor in Stanford’s Graduate School of Education.
It allows large numbers of users to remotely conduct experiments on one-celled organisms over the Internet and to collect, analyze and model the data.
The project comes as education departments in several states begin to adopt the Next Generation Science Standards, new U.S. guidelines for K-12 science education that aim to boost the nation’s scientific literacy.
Blikstein said the Biology Cloud Lab can put many of the most sophisticated aspects of the new science standards within reach of many students for the first time.
As configured in prototype on the Stanford campus, the lab’s experiments involve common pond-dwelling organisms called Euglena that convert light to energy. Students and teachers use remote-control software to manipulate light sources around microfluidic chips full of Euglena communities. As users apply various amounts of light stimuli to attract or repel the Euglena in the Stanford lab, a webcam microscope live-streams the operation back to them.
The key was in developing biotic processing units to hold the organisms and record the data for remote users, and in developing algorithms that would allow many remote users to run experiments over time.
The project has successfully been tested on middle-school students. Built at scale – 250 biotic processing units in one small room – each lab could host 1 million experiments at a cost of one cent each.
“We are doing to biology what Seymour Papert did to computer programming in the 1970s with the Logo language,” Blikstein said.
“The Biology Cloud Lab makes previously impossible activities easy and accessible to kids – and maybe also to professional scientists in the future.”
Learn about the team’s other projects for interactive biotechnology education.
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Stanford University opened in 1891 to “promote the public welfare by exercising an influence in behalf of humanity and civilization,” in the words of founders Jane and Leland Stanford.
For 125 years, Stanford students and scholars have pursued these lofty goals. They have developed ways to improve human understanding of one another, to care for our planet and to foster opportunity for everyone.
Watch some highlights of this 125-year journey on a Stanford video produced for the 2016-17 athletic season.
The video includes such landmarks of innovation as the Palo Alto garage where, in 1939, Stanford graduates William Hewlett, ’34, ENG ’39, and David Packard, ’34, ENG ’39, founded the company now known as HP to develop Hewlett’s graduate-school project, the precision audio oscillator.
Also featured are some of the Stanford people who have put their knowledge to work in the world:
Arthur Kornberg, professor emeritus of biochemistry at the School of Medicine, shared the 1959 Nobel Prize in Physiology or Medicine for the test-tube synthesis of DNA – and begat another Nobel laureate, his son Roger Kornberg, PhD ’72, who became a Stanford professor of structural biology and was awarded the Nobel in chemistry in 2006.
Biology and neurobiology Professor Carla Shatz works to restore brainpower at a molecular level. As director of the life science institute Bio-X, Shatz leads a larger initiative to amplify Stanford’s research power by uniting its scientists in interdisciplinary collaboration to tackle life sciences’ toughest problems.
Stanford pediatrician Paul Wise and the Guatemala Rural Child Health and Nutrition Program have saved hundreds of children’s lives by slashing nutrition-based mortality and speeding the training of local health workers through technology.
Explore a timeline of other great Stanford moments over 125 years.
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Beloved Stanford sculpture inspires reflection and honors resilience
“I was baffled by the beauty that erupted from these moving colors. … For the first time in my life, I saw beauty in finance. Even in the depth of money and numbers, someone managed to bring out emotion.”
— Louise Fleischer, graduate student in aeronautics/astronautics
The earliest sculptures in Stanford’s famed outdoor collection proclaim the university’s mission by depicting individuals: the Stanford family, its founders, and such great minds as Johann Gutenberg and Benjamin Franklin, enshrined in white marble atop what is now Wallenberg Hall.
Key to all these individuals – and to Stanford – are qualities of creativity, persistence and resilience. They too, are evoked by a sculpture on campus, only five years old but already a Stanford favorite.
Peter Wegner’s Monument to Change as it Changes occupies a wall outside Zambrano Hall of the Graduate School of Business’ Knight Management Center. It consists of 2,048 sets of colored chips programmed to flip into varying patterns, each pattern unique within an eight-hour period.
Wegner, who created Monument to Change as it Changes among a suite of companion works for the Knight Management Center’s 2011 opening, lists its materials as “steel, polycarbonate and time.”
“Usually, monuments commemorate past events,” Wegner wrote after the sculpture’s unveiling. “But what if a monument instead commemorated the process of change?
“European train stations use this technology to announce arrivals and departures. Here, the destination is always color and always changing.”
Monument to Change as it Changes honors the GSB’s commitment to preparing its students for a future that is always changing. It’s a commitment shared throughout Stanford. Accordingly, people throughout Stanford find meaning in its procession of colors and serenity in the gentle clicking of the chips as they move.
Sarah Beller, ’15, chanced upon the piece as a stressed-out freshman exploring the campus.
“As the pieces spin, the cards make a soft clicking sound, which reminds me of the rain on my roof at home.
“That day freshman year I spent an hour sitting in front of the piece watching and listening as the patterns moved.”
After two years, Monument to Change as it Changes is still my favorite piece of art at Stanford, and it has become my go-to spot when I need to calm down and take a break from school and work.”
— Sarah Beller, ’15
It and the other Wegner pieces at the GSB are distributed in a town-square atrium that encourages members of the community to pause and enjoy their surroundings.
Through Monument to Change as it Changes, they learn that a concept or value can be expressed in many ways: through the arts, through product innovation, through leadership.
It encourages them to stop and think, a first step before creativity can begin.
Learn more about Monument to Change as it Changes and its companion works by Peter Wegner at the Knight Management Center. Find them on this GSB map.
Download a podcast of this and other recent art acquisitions throughout campus, with an accompanying map.
Learn more about outdoor sculpture at Stanford on the Campus Arts Map.
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Stanford is well-known for its undergraduate and graduate education. Yet here in the heart of Silicon Valley, it’s not surprising to learn the university is also home to a virtual high school. Founded in 2006, Stanford Online High School (OHS) has grown from graduating two seniors in 2007 to 49 in 2015. This year marks a milestone as the school will celebrate 10 years of granting diplomas with the Class of 2016.
One of the nation’s first independent online high schools, Stanford OHS draws students from more than 45 states and 27 countries. It serves students seeking rigorous coursework and an innovative learning environment, as well as those spending significant time on pursuits outside the classroom.
“Stanford OHS attracts academically talented students from around the world, many of whom are pursuing passions outside of school in sports, arts and business,” said MaryAnn Janosik, Stanford OHS head of school.
Students follow a flexible college-style schedule and can be enrolled full-time, part-time or in a single course. In addition, the school offers a full complement of academic advising, counseling and college counseling services.
The school’s online teaching and learning model actively defies the typical notion of isolated students listening to recorded lectures. Advanced video conferencing technology allows students and instructors to see each other in classes held in real time. The interaction is lively. Students debate, discuss, question and respond in the same virtual space. Technology facilitates the collaboration. Students comment through a running text chat, annotate onscreen materials and share ideas on a common whiteboard.
Building a thriving intellectual and extracurricular community is a top priority for the school. Many students begin the year with a Summer@Stanford residential program. For two intensive weeks, they get to know their instructors, engage in hands-on learning and develop bonds with their classmates. Students and families gather for meetups in locations around the globe, and immersion travel provides additional opportunities to connect. Student-driven extracurricular activities include debate, Model U.N., newspaper, yearbook, and language clubs – in short, much of what is found in a traditional high school.
Stanford OHS is part of Stanford Pre-Collegiate Studies, which offers a range of programs for pre-college students. In High School Summer College, students take university courses together with Stanford undergraduates. Pre-Collegiate Institutes immerse students in the study of humanities, mathematics, medicine and the arts. Additional opportunities include online university-level math and science courses.
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Thank you for visiting the Stanford 2D Trends website! The purpose of this site is to compile and summarize the "trends" in
2D Semiconductor device research. Our hope is that this
website will facilitate important discuss within the 2D device community and help researchers benchmark their results.
We also recognize our data is not an exhaustive search of all the reports on 2D transistors. If you think there is research
not shown here that should be represented, please contact us. We look forward to your thoughts and comments.
The most important thing to remember when looking at this website is that
correlation is NOT causation. Just because the data shows
a trend, does not mean there is a correlation between the x and y variables. Correlation could emerge from circumstantial causes,
such as maybe there is a trend that decreasing contact spacing actually decreases Ion. This does not make sense in the context of
resistance and could be because fabricating shorter channel lengths is more difficult and leads to poorer device characteristics.
For citing this work, please use this format:
C.J. McClellan, S.V. Suryavanshi, C.D. English, K.K.H. Smithe, C.S. Bailey, R.W. Grady, E. Pop ,
"2D Device Trends," Accessed on: MM/DD/YYYY. [Online]. Available: http://2d.stanford.edu/2D_Trends.html and replacing the access date
with the date you accessed 2D Trends data for your publication. Including the correct access date is important as we routinely update 2D Trends
data and your citation should reflect the data presented on the website at that time, which can be adjusted with the "Select Access Date:"
input on the main page.
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Thank you for visiting the Stanford 2D Trends website! The purpose of this site is to compile and summarize the "trends" in
2D Semiconductor device research. Our hope is that this
website will facilitate important discuss within the 2D device community and help researchers benchmark their results.
We also recognize our data is not an exhaustive search of all the reports on 2D transistors. If you think there is research
not shown here that should be represented, please contact us. We look forward to your thoughts and comments.
The most important thing to remember when looking at this website is that
correlation is NOT causation. Just because the data shows
a trend, does not mean there is a correlation between the x and y variables. Correlation could emerge from circumstantial causes,
such as maybe there is a trend that decreasing contact spacing actually decreases Ion. This does not make sense in the context of
resistance and could be because fabricating shorter channel lengths is more difficult and leads to poorer device characteristics.
For citing this work, please use this format:
C.J. McClellan, S.V. Suryavanshi, C.D. English, K.K.H. Smithe, C.S. Bailey, R.W. Grady, E. Pop ,
"2D Device Trends," Accessed on: MM/DD/YYYY. [Online]. Available: http://2d.stanford.edu/2D_Trends.html and replacing the access date
with the date you accessed 2D Trends data for your publication. Including the correct access date is important as we routinely update 2D Trends
data and your citation should reflect the data presented on the website at that time, which can be adjusted with the "Select Access Date:"
input on the main page.
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This repository contains the source codes for the paper Choy et al., 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, ECCV 2016. Given one or multiple views of an object, the network generates voxelized ( a voxel is the 3D equivalent of a pixel) reconstruction of the object in 3D.
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2). The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data using 3D-Convolutional LSTM which allows attention mechanism to focus on visible parts in 3D. Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid. Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing. Our extensive experimental analysis shows that our reconstruction framework i) outperforms the state-of-theart methods for single view reconstruction, and ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).
If you find this work useful in your research, please consider citing:
@inproceedings{choy20163d,
title={3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction},
author={Choy, Christopher B and Xu, Danfei and Gwak, JunYoung and Chen, Kevin and Savarese, Silvio},
booktitle = {Proceedings of the European Conference on Computer Vision ({ECCV})},
year={2016}
}
Left: images found on Ebay, Amazon, Right: overview of 3D-R2N2
Traditionally, single view reconstruction and multi-view reconstruction are disjoint problems that have been dealt using different approaches. In this work, we first propose a unified framework for both single and multi-view reconstruction using a 3D Recurrent Reconstruction Neural Network
(3D-R2N2).
We can feed in images in random order since the network is trained to be invariant to the order. The critical component that enables the network to be invariant to the order is the 3D-Convolutional LSTM
which we first proposed in this work. The 3D-Convolutional LSTM
selectively updates parts that are visible and keeps the parts that are self-occluded.
Selective update or attention is the crucial component that enables 3D-R2N2 to resolve multiple viewpoints seamlessly.
3D-Convolutional LSTM works like the following: If the input image is taken from the front/side view, the input gates correspond to the front and side view activates (opens). If the view of an object taken from the back is fed into the network, the input gate will open up for the voxels on the back. This operation allows the network to put image features to the right position.
We used two different types of networks for the experiments: a shallow network (top) and a deep residual network (bottom).
We provide source codes for the project on http://github.com/chrischoy/3D-R2N2.
We used ShapeNet models to generate rendered images and voxelized models which are available below (you can follow the installation instruction below to extract it to the default directory).
MIT License
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What does it take to develop an agent with human-like intelligent visual perception? The popular paradigms currently employed in computer vision are problem-specific supervised learning, and to a lesser extent, unsupervised and reinforcement learning. However, we argue that none of these would lead to truly intelligent visual perception unless the learning framework is specifically devised to gain abstraction and generalization power. Here we show our approach to this problem which is inspired by the developmental stages of vision skills in humans. Specifically, rather than training a new model for every individual desired problem, we train a model to learn fundamental vision tasks that serve as the foundation for ultimately solving the desired problems. As our first effort towards validating this approach, we employ this method to learn a generic 3D representation through supervising two basic but fundamental 3D tasks. We show that the learned representation generalizes to unseen 3D tasks without the need to any fine-tuning while it achieves a human-level performance on the task it was supervised for.
Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to unseen tasks and abstraction capabilities can be achieved. We use this approach to learn a generic 3D representation through solving a set of supervised proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching (please see the paper for a discussion on how these two tasks were selected). We empirically show that the internal representation of a multi-task ConvNet trained to solve the above problems generalizes to unseen 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need to fine tuning and shows traits of abstraction abilities (e.g., cross modality pose estimation).
In the context of the supervised tasks, we show our representation achieves state-of-the-art wide-baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also demonstrate 6DOF camera pose estimation given a pair local image patches.
We contribute a large-scale dataset composed of object-centric Street View scenes along with point correspondences and camera pose information. We collected the dataset by integrating Street View images, their metadata, and large-scale geo-registered 3D building models scraped on the web. The simplified steps of collecting the dataset is show in the above animation (see the paper for details). The dataset is available to public for research purposes and currently includes the main areas of Washington DC, New York City, San Francisco, Paris, Amsterdam, Las Vegas, and Chicago. To ensure the quality of the test set and keep evaluations unimpacted by potential errors introduced by the automated data collection, every datapoint in the test set are verified by at least three Amazon Mechanical Turkers. The procedure and statistics are elborated in the supplementary material.
DOWNLOAD THE DATASET AND INSTRUCTIONS [HERE]
DOWNLOAD VISUALIZATIONS AND ACCURACY ANALYSIS OF THE TEST SET [HERE]
Each row depicts a sample collected image bundle. Each bundle shows one target physical point (placed in the center of the images) from different viewpoints. Below you can see a few snapshots of 3D models of the 8 cities using which the dataset was collected. You can see more snapshots here. The 3D models are also available for download.
We investigate the properties of the learned representation using the following methods:
The 2-dimensional embeddings (tSNE) of our representation for MIT places dataset (‘library’ category) and an unseen subset of our dataset are provided below. The representation organizes the images based on their 3D content (scene layout, relative camera pose to the scene, etc) and independent of their semantics (visible objects, architectural styles) or low-level properties (color, texture, etc). This suggests that the representation must have a notion of certain basic 3D concepts, though it was never provided with an explicit supervision for such tasks (especially for non-matching images, while all tSNE images are non-matching). The tSNE of our dataset also suggests the patches are organized based on their coarse surface normals (again, a task that the representation didn’t receive a supervision for). See the section below for quantitative evaluation of our representation for surface normal estimation on NYUv2 dataset.
Click on a query image to see its NNs based AlexNet (trained on ImageNet) and our representation. Note the geometric consistency between the NNs and their respective query.
We evaluated our representation on NYUv2 benchmark to see if it has a notion of surface normals (see the discussion on the tSNEs above). The summary of the results are provided below, showing our representation outperforms the baselines on unsupervised surfance normal estimation (see the paper for more details and additional results).
The following figures shows the tSNE embedding of several ImageNet categories based on our representation and AlexNet trained on ImageNet. Please see the paper for the tSNEs of other baseline representations. The embeddings of our representation are geometrically meaningful, while the baselines either perform a semantic organization or overfit to other aspects, such as color. NOTE: certain aspects of object pose estimation, e.g. distinguishing between the front and back of a bus, are more of a semantic task rather than geometric/3D. That adversely impacts a method that has a 3D understanding but not semantic (e.g., our representation). In this sense, the poses that are 90 degrees congruent could be considered identical and equally good (i.e., different sides of an even cube).
The following figure shows cross-category NN search results for our representation along with several baselines. This also evaluates a certain level of abstraction as some of the object categories can be drastically different looking. We also quantitatively evaluated on 3D object pose estimation on PASCAL3D with the results available in the following table. Our representation outperforms scratch network and comes close to AlexNet that has seen thousands of images from the same categories from ImageNet and other objects.
Quantitative results on PASCAL3D benchmark
We evaluated our representation on LSUN dataset. The right table provides the results of layout estimation using a simple NN classifier on our representation along with two supervised baselines, showing that our representation achieved a performance close to Hedau et al.'s supervised method on this unseen task. The left table provides the results of 'layout classification' using NN classifier on our representation and to AlexNets FC7 and Pool5 representations.
We performed an abstraction experiment on layout estimation (similar to the one on 3D object pose shown above). We performed NN retrieval between a set of 88 images showing the interior of a synthetic cube and the images of LSUN dataset. The same observation of the abstraction experiment on 3D object pose is made here as well with our NNs being meaningful while the baselines mostly overt to appearance with no clear geometric abstraction trait.
The qualitative and quantitative results of evaluations on the supervised tasks can be seen below. We used the standard evaluation protocols for both camera pose estimation and feature matching tasks. We also provide evaluation results on the (non-Street View) benchmarks of Brown et al. and Mikolajczyk&Schmid.
Follow the instructions to upload a pair of images. Press Run and the relative camera pose and matching score between the two will be shown.
You can also upload a batch of (<100) images and receive the 2D embedding of our representaion vs the baselines reported in the paper.
"Generic 3D Representation via Pose Estimation and Matching", Amir R Zamir, Tilman Wekel, Pulkit Agrawal, Jitendra Malik, Silvio Savarese, in ECCV 2016. [Paper] [Supplementary Material]
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About
"There is just something comforting about the atmosphere surrounding the A³C."
MICHAEL, '10, MA '11
The A³C builds a campus-wide community of students, faculty, staff and alumni that fosters greater understanding and awareness of the Asian experience in America. It offers many resources for the community. The A³C is home to over thirty student organizations that hold weekly meetings and rehearsals in the center and also use the office as workspace for planning events.
The center houses the Asian American Resource Library which contains Asian American literature, reference texts, hard-to find-periodicals, university documents, newspaper clippings and videos. Located in the center for student use are a computer cluster, TV, video conferencing equipment, and sound system.
Students come to the A³C for information on campus resources and community service opportunities; for meetings; for cultural and educational programs and workshops; for research materials; for organizational and personal advising; for relaxing between classes; and to study. In the evenings, student organizations utilize the space for group meetings and events. Staff come to the A³C to attend events, meet as staff and connect with and mentor students. Faculty come to the A³C for resources, help with research projects and to speak at workshops and on panels. Alumni come to the A³C to meet students and to host meetings and events. Campus partners come to the A³C for advice, collaborations and to connect with students.
Mission
The Asian American Activities Center, A³C, is a department under the Vice Provost of Student Affairs and serves as Stanford’s primary resource for Asian and Asian American student affairs and community development. The A³C contributes to the academic mission of the University through its partnerships and collaborative work with faculty, departments and academic programs. Through programming and advising, the center facilitates the multicultural education of all students and the development of leaders who are able to negotiate an increasingly diverse and complex workplace and global environment
History
The Asian American Activities Center has a long history at Stanford as a student-initiated space that has transformed over the years to meet the current needs of the Stanford student body. The first iteration of the center, the Asian American Resource Center, began in 1972 after a group of students involved in the newly formed Asian American Students Alliance (AASA) advocated for and received an office space in the Old Fire Truck House. For the first decade of its existence, the center was staffed entirely by five student volunteer interns. In 1977, the name of the center was changed to the Asian American Activities Center.
In 1987, the Dean of Students approved funding for a half-time director/dean position in response to a set of demands proposed by the Rainbow Agenda (including students from AASA, MEChA, SAIO and BSU). Julian Low served in this inaugural role and was supported by Elsa Tsutaoka as the Office Manager. In 1989, the Dean of Student Affairs formally institutionalized the A³C by hiring Richard Yuen as the first full-time director. Soon after in 1991, Cindy Ng was hired as the first Program Coordinator, from which she was promoted to Assistant Director and then to Associate Dean and Director. Shelley Tadaki '00, MA'03 served as the Associate Director of the center from 2004-2012. Following Shelley's departure, Jerald Adamos was hired in 2012 as the Associate Director and then received a title change that recognized the Assistant Dean's responsibilities connected to the role. In 2018, the Vice Provost for Student Affairs approved funding for a third full-time staff position and Latana Thaviseth was hired as the Assistant Director.
In 2020, Stanford alumni and siblings Will Hsu '98 and Angie Hsu '96, MA '96 established the Scott J. J. Hsu Directorship Fund for the Asian American Activities Center to honor their father, Scott J. J. Hsu, which created the first endowed directorship for the Stanford Community Centers. In the same year, Cindy also received the Amy J. Blue Award, the highest recognized honor for staff members at the University. Cindy retired from Stanford in 2022 as the Associate Dean of Students and Inaugural Scott J.J. Hsu Director of the A3C and received Emeritus status from the University. Linda Tran '06, MA '07 was hired in the following Fall and began her role as the Associate Dean of Students and Scott J.J. Hsu Director of the A3C. In 2023, the Stanford Community Centers received temporary funding to hire a fourth full-time staff position, and Sunny Trivedi, PhD '23 was hired as the second Assistant Director. In 2024, Mary Q. Tran stepped into the Assistant Director position after the departure of Latana Thaviseth.
On Stanford's main campus at the edge of White Plaza, the Asian American Activities Center (A3C) is now located in the Clubhouse Building of the historic Old Union complex and is a university department within the Centers for Equity, Community, and Leadership (ECL) in the Division of Student Affairs.
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Advisory Board
"As a first-generation college student, my parents were apprehensive about me leaving to attend school so far away. From my first moments on the Stanford campus, the A³C played a key role in integrating me into the university."
MICHAEL '11 MS '11
Main content start
Who We Are and What We Do
The Asian American Activities Center Advisory Board serves to promote and support the work of the center. This includes fundraising, advocacy for needed student services, and advising on the overall direction of the A³C. The board is composed of undergraduate and graduate students, staff, alumni and faculty.
Undergraduate Student Representative
- Jenny Duan '26
- Sandi Khine '25
- Phong Nguyen '25
Graduate Student Representative
- Stephen Han
Faculty Representative
- Jeanne Tsai
Asian Staff Forum (ASF) Representative
- Jack Tse
Filipino American Community at Stanford (FACS) Representative
- Eunice Delumen
Stanford Asian Pacific American Alumni Club (SAPAAC)/Alumni Representative
- Kuldip Ambastha '04
Asian American Students' Association (AASA), Ex-Officio
- Abhi Boda '27
- Brett Han '27
Asian American Activities Center (A3C), Ex-Officio
- Linda Tran
- Mary Tran
- Sunny Trivedi
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Our Community
"By providing a space for students to grapple with thorny questions about identity, race and class, the A³C and its fellow community centers are able to develop leaders with a more mature sophisticated understanding of how to work with diverse communities."
DANIEL, '10
Community Involvement: What Will It Mean to You?
For many, a Stanford experience is not complete without community involvement. Participation in co-curricular activities is an important avenue for students to gain valuable experiences and knowledge that cannot be found in the classroom. Stanford offers a wide variety of opportunities for students, and many of them are within the Asian American community.
Asian and Asian American Organizations
With many Asian and Asian American organizations on campus, we hope you will find one that matches your interests. Students are constantly creating new cultural, social, political, religious and service-oriented groups to address the changing needs of the community.
The Asian American community continues to flourish through the hard work and dedication of each group. So, take advantage of these opportunities at Stanford!
All of us find our own answers to the question, "What is Stanford?" The following is one of A³C’s previous staff member’s answer to this question.
What Is Stanford?
“Stanford is TCS dumplings served fresh and tender in Tresidder Oak. Stanford is SVSA presentations on Agent Orange and stories of its survivors. Stanford is Magic Mic karaoke with PASU, partition discussions with Sanskriti, Boehemian Jam and identity talks at Okada House. Stanford is the A³C pronounced “A cubed C,” the paradoxical Listen to the Silence concert, the Asian American Studies class that exposed us to Jhumpa Lahiri before she got popular. Stanford is, among boundless other things, her Asian American community. To all of this, we welcome you!
Of course, I know that Stanford’s Asian American community contains so many groups, Greeks and grassroots organizations that it can get a little overwhelming. Maybe it feels scary (“Do any of these groups represent/accept me?”), maybe it feels irrelevant (“Uh, I just came to major in Electrical Engineering.”), and certainly, having been pre-med, I know both feelings. But as you probably know, no matter your major, classes offer only so much. Whether you’re talking about the mentorship by AIM or direct action organizing by SAAAC, the community offers major venues to develop leadership skills, professional connections and great friendships. Furthermore, the community is a conduit that connects with the African American, Chicano/a, Native American, Women’s and LGBT communities for greater common understanding.
But most importantly for me, the communities at the A³C and Okada have been a home away from home, a safe place where I’ve been nurtured and also offered a space to ask challenging questions about myself and the world. Since freshman year, I’ve treasured this family, and it’s still my rock. My base. Where my questions get answered and where I find support.
Please, drop by the A³C. Hang out at Okada. Check out what everyone has to offer. Who knows—maybe you too will fall in love.”
Takeo Rivera, '08
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Skip to main content
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Learn More About Asian American & Pasifika Heritage Month Here
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"I learned so much about Stanford’s Asian American community and about myself while I worked at the center as a student staff member."
CHUYI '20
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"I learned so much about Stanford’s Asian American community and about myself while I worked at the center as a student staff member."
CHUYI '20
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New Assistant Director joins the A3C!
We are pleased to announce the recent appointment of Mary Q. Tran as the new Assistant Director at the Asian American Activities Center!
Mary Q. Tran (she/they, chi/chanh) brings deep passion and experience working in higher education on issues of student transition and retention, college access and equity, and bridging the P-20 pipeline, with an emphasis on first-generation and/or low-income (FLI) students of color. Rooted in the ethos of cura personalis or ‘care for the whole person,’ Mary’s student development approach prioritizes students’ holistic growth, success, and persistence. She has extensive experience leading mentorship and coaching initiatives for new, transfer, and first-generation students. This includes serving in the College Possible AmeriCorps program as a college coach for underrepresented and/or FLI college students across the University of Washington campuses.
Most recently, Mary was the inaugural Program Coordinator at San José State University’s new Center for Asian Pacific Islander Student Empowerment (CAPISE) where she developed inclusive programming, including a peer mentorship program for first-year API students. In this role, she also partnered with the university’s Alumni Association to found the API Alumni Network (APIAN), an affinity group for Asian & Pacific Islander SJSU alumni, and currently serves as APIAN’s inaugural Co-Chair. Mary received her BA in Child and Adolescent Development from San José State University, and a M.Ed in Student Development Administration from Seattle University.
She is excited for the opportunity to continue to mentor and guide students through their identity exploration at Stanford, and to learn from and build off of the A3C’s long-standing work. Born and raised in San José, CA, Mary is the daughter of Vietnamese immigrants. She loves all things arts and crafts, reading, and spending time with her family of 2 dogs alongside her spouse.
Mary assumes the Assistant Director role following the departure of Latana Thaviseth, our beloved former Assistant Director of six years. Please join us in congratulating and welcoming Mary to Stanford!
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Alexa Tran '27
Ethnicity: Vietnamese
Major: Computer Science
Hometown: Fresno, CA
Why you joined the A³C: I joined the A3C to find a community here on campus that cares about the same things I do. It's been so rewarding getting to know so many cool people who are so passionate about creating a space for Asian and Asian American students here at Stanford, and I've truly found such great friends and mentors in this space.
One thing you love about Stanford: I love the endless opportunities I have here! I often find myself having to narrow down the things I want to be involved in, which is a really great problem to have.
One class/program to participate in before leaving Stanford: The A3C Frosh Intern program!!!!!!!! I met such good friends and it was a great way to learn how to get involved so early on in my time at Stanford.
Advice to Frosh/anyone on campus Some advice I have for Frosh is to try everything. Branch out and explore different classes, clubs, etc. because it takes a lot of exploration to find out what you truly like and dislike! After a little bit of this, you can figure out what you want to dedicate your time to.
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Asian American New Student Orientation
"Growing up in the Midwest did not put me in touch with many Asian Americans, and I did not deal very well with my bi-cultural identity. It was not until I joined A³C that I met other Asian Americans who had similar experiences to me."
I-CHANT PH.D. '10
All 2024 New Student Orientation (NSO) events (virtual and in-person) will require registration to attend. To register, please check out Stanford University's New Student Orientation.
Community Welcome
Tuesday, September 17, 1:00-3:00PM PST @ Clubhouse Ballroom (Clubhouse, Room 100) in the Old Union Complex
Kick off NSO with the Asian American Activities Center (A3C) and hear from your members of the Asian American community - all are welcome!
Welcome to the Family
Friday, September 20, 4:30-6:00PM PST @ the Asian American Activities Center (Clubhouse, 2nd Floor and Ballroom) in the Old Union Complex
Come to learn about the Asian American community and talk to other frosh and upperclass students while doing arts and crafts!
Okada Chillout
Saturday, September 21, 9:00-11:00PM PST @ Okada side of Wilbur Courtyard and Okada Lounge
Explore Stanford’s Asian American themed dorm, meet fellow frosh and upperclass residents, and get a taste of living in an ethnic theme dorm!
A3C Speaker Series: "You're Here!" - Student Mixer
Thursday, September 26, 4:30-5:30PM PST @ A3C Couchroom, 2nd Floor of the Clubhouse in the Old Union Complex
You made it! Take a breather and meet some of our featured student speakers to hear about how their passions shaped their experiences and how you can get involved in our Stanford Communities.
A3C Speaker Series: Stanford and More
Thursday, October 3, 4:30-5:30PM PST @ A3C Couchroom, 2nd Floor of the Clubhouse in the Old Union Complex
Don’t be scared, we’re here to help. With so many academic and community resources here at Stanford, where do you even begin? Come build connections with Stanford faculty and staff as they share their strategies and resources for making the most of your Stanford career in a low-stakes environment.
AASib SibFam Reveal
Friday, October 4, 4:30-5:30PM PST @ Roble Field
Sign up for the AASib Program in advance! Your Sibfam will be revealed and you will get to meet your co-lil sibs and big sibs! Advanced sign ups are mandatory!
More Community Events to come!
The Asian American community will be hosting more events throughout the first weeks of fall quarter for you to get to know each other! Please continue to check this page for updates.
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Letter from AANSOC Coordinator
"Because of the time I spent at the A³C, I developed self confidence and leadership, but also an appreciation for diversity and an understanding of how to overcome personal adversity."
PAHUA '10
Dear Incoming Students,
Welcome to Stanford! Let me begin by extending a warm congratulations on your acceptance to Stanford. But before embarking on your exciting Stanford journey, I encourage you to take time to celebrate, rest, and spend time with loved ones.
My name is Lilian, and I am a sophomore from Fresno, California. I am thrilled to be your 2023 Community Coordinator for the Asian American New Student Orientation Committee (AANSOC). I know from personal experience how overwhelming NSO can be, so I’d like to take a moment to share some of my own identity and first memories of the Asian American Community at Stanford.
In truth, for the greater part of my life, I experienced a distinct absence of a cohesive Asian American community. It was not until high school, when the Fresno Burmese Community bought a historic temple downtown–the first physical space we had to congregate–that I was able to fall in love with my cultural identity and by extension myself, understand that this was what a community was–and how beautiful and precious that it could be.
Thinking about Stanford and finding a place for myself naturally filled me with trepidation; and yet, there was a keen sense of excitement towards engaging in the very deliberate spaces for the Asian American community, something that had pervaded me for most my life. Now having spent a year at Stanford, let me assure you–no matter your background or your personal relationship with your Asian American identity–there is a space for you here. Stay a while, and you too will find that the Asian American communities here are deeply intentional, tender, warm and resilient. We rely on one another, make space to learn and grow, have assurance in our collective strength, love and cherish deeply in capacities previously unknown to us, and along the way come to uncover yet undiscovered facets of ourselves.
From being part of an Asian American Sibling (AASib) family to joining any of the affiliated Voluntary Student Organizations (VSOs) to simply attending one of the countless events, there are many entry points into the Asian American community–all for your taking. To be in community at Stanford means investing in your continued growth, and being willing to grow is something that will take you far!
I want to end this letter by formally inviting each and every one of you to our AANSOC events from September 20-October 5, 2023. A calendar of event times, locations, and descriptions can be found in this packet and on our website for the most up-to-date information as well. These events are free and open to all students, so please invite any new friends who might be interested as well! We hope these events give you an introduction to the diverse and vibrant Asian American community at Stanford, and if you want to engage with the Asian American community before arriving, feel free to explore the A3C website (www.a3c.stanford.edu), “like” our Facebook page, or follow our Instagram “@stanforda3c.” I am also more than happy to personally connect, answer questions, or provide a friendly face! You can reach me at lilianch@stanford.edu.
Finally, I want to leave you with some reassurance; entering college can be uncertain and stressful, but you are strong and more than capable. The Asian American community at Stanford is here for you, and we excitedly welcome you with open arms.
Warmly,
Lilian Chen
Stanford Class of 2026
2023 Asian American New Student Orientation Committee (AANSOC) Coordinator
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Sign Up to be a Lil Sib
"No doubt, twenty years after I have left the farm, my strongest tie to Stanford is still rooted in my connection to the A³C."
MAE ’92 MA ‘93
What Is a Lil Sib?
A Lil Sib is a new student, usually a frosh or transfer student. Lil Sibs are interested in the various components of the Asian American community on campus and want to meet people who can expose them to new experiences.
What Are AASib Families?
AASib families (SibFams) are composed of up to four Big Sibs (upperclassmen) and at least one Lil Sib. SibFams are designed to help integrate incoming frosh and transfers into the Asian American community, and the undergraduate community at large, at Stanford. Being an incoming freshman can be daunting, and your SibFam is meant to help you transition through this exciting process.
Matching Process
Lil Sibs are matched with SibFams who have similar academic and social interests based on the application you submit. Depending on your SibFam, many may engage in different activities, like sporting activities, or trips off campus for Asian food, or maybe a night of watching movies in the dorm. AASib holds several annual events to help SibFams get together, such as social events with ice cream and a day of field games.
Interested in Becoming a Lil Sib?
The Online applications to be a Lil Sib is available for new Frosh and Transfer Students.
Priority decision for Lil Sibs will be Friday, September 20, 2024 @ 11:59 PM PST. If students miss the opportunity to apply by the September 9 deadline, we can still accept applications until Friday, September 27th @11:59 PM PST.
Students will be matched with their Big Sibs/SibFams before the AASib Family Reveal event on Friday, October 4th as part of the last Asian American New Student Orientation Committee (AANSOC) events after New Student Orientation (NSO). Students would then be notified after the final deadline with their matched Big Sibs/SibFams.
For more information or if you have any questions, contact the AASIB Core Leaders.
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Asian American Admit Weekend
"For many, the A³C symbolizes the heart of the Asian American community at Stanford. For me, it served as a safe space to grow, learn and feel welcomed."
CHUYI '20
All admitted students are invited to attend Admit Weekend, a two and a half day, on-campus program in April. All interested participants must register through Stanford's Admit Weekend page to learn about the specific location for each event.
Schedule
Thursday, April 24, 2025
Okada Chillout, 9:00-10:30 p.m. PT
Enjoy some tasty treats and meet current residents of Stanford’s Asian American ethnic theme dorm, Okada! We’ll share our experiences living in one of Stanford’s four-class ethnic theme dorms and also answer any questions you have about living and learning with the Asian American community at Stanford. Admits only. Visit the Okada Dorm website for more information in the meantime!
Friday, April 25, 2025
Asian American Community Welcome, 3:30-5:00 p.m. PT
Come learn about Stanford's Asian American Activities Center (A3C) and get an overview of the broader Asian American community at Stanford. Prospective Students (ProFros) will have an opportunity to spend time with some of our student leaders to understand why the Center and community are important to them!
Saturday, April 26, 2025
Asian American Activities Center Café Hour, 9:30-10:30 a.m. PT
Eat, Meet, & Greet! Start your Saturday morning by stopping by the Asian American Activities Center (A3C) for light breakfast snacks and Asian treats. Meet and mingle with other ProFros and get a brief introduction to the space, programs, and people at the A3C. Admits only.
Community Hangout Night!
The Asian American community at Stanford is incredibly diverse. On Friday night of Admit Weekend (April 25, 2025), come hang out with leaders in our student groups to learn about their experiences and how you can find community once you’re on campus! Admits only.
2025 Schedule: Friday, April 25, 2025
- Momo Meetup, 6-7 p.m. PT
Hosted by the leaders of the Stanford Nepali Student Association - Teatime with Stanford Myanmar, 7-8 p.m. PT
Hosted by the leaders of the Stanford Myanmar Student Association - Cambodian Cardinal Boba & Brush, 7-8 p.m. PT
Hosted by the leaders of the Cambodian Student Association - Hmong Admit Welcome, 7-8 p.m. PT
Hosted by the leaders of the Hmong Student Union - NSU Matcha Mixer, 7-8 p.m. PT
Hosted by the leaders of the Nikkei Student Union - Tea, Totes, & Tibet, 7-8 p.m. PT
Hosted by the leaders of the Tibetan Student Union - Island Time and Musubi State of Mind, 7-8 p.m. PT
Hosted by the leaders of the Pacific Islander Student Association - SVSA Ché Thái and Game Night, 8-9 p.m. PT
Hosted by the leaders of the Stanford Vietnamese Student Association - Bangali Cha & Chill, 8-9:30 p.m. PT
Hosted by the leaders of the Bangladeshi Students Association at Stanford - Lao Admit Sabaidee, 8-10 p.m. PT
Hosted by the leaders of the Lao and Laotian Student Union
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Asian American & Pasifika Heritage Month
This year’s Asian American & Pasifika Heritage Month theme, "A Dream Worth Fighting For: Rekindling Solidarity and Resistance," calls for collective action and unity against the growing threats of imperialism and oppression.
Asian American & Pasifika Heritage month (or more commonly Asian American & Native Hawaiian Pacific Islander (AANHPI) Heritage Month) is celebrated in May to commemorate the achievements and contributions of people of Asian and Pacific American descent in the United States.
More About the Asian American & Pasifika Heritage Month
A Dream Worth Fighting For: Rekindling Solidarity and Resistance
This year’s Asian American & Pasifika Heritage Month theme urges collective resistance and solidarity against the rising tide of imperialism and repression. As war, genocide, and attacks on people of color, transgender and nonbinary individuals, immigrants, and student activists escalate—including the forcible disappearance of student activists like Yunseo Chung, Ranjani Srinvasan, and Mahmoud Khalil—we recognize the urgency of collective action. The dismantling of education, the erasure of marginalized voices, the violent suppression of dissent, and the complicit silence of institutions in the face of this injustice demand a response. We will not be silent.
Now more than ever, we must rekindle solidarity, challenge dehumanization, and reclaim the power to dream and build alternative futures. Heritage Month groups together very different communities, from Pasifika peoples to the diversity of our specific Asian diasporas, but it is through joined struggle against war, racism, and displacement that we find solidarity and unity. A world free from militarism, incarceration, and systemic oppression is not only possible, but necessary. Our responsibility—whether we be scientists, artists, workers, or students—is to build it. We all have a place in this movement. This month, discover in one another a renewed commitment to create and fight for a world worth dreaming of.
To have your events included on this year's calendar, please submit your event by Wednesday, April 16, 2025 at 12:00 p.m. through our submission form. All events through the spring quarter (April through mid-June 2025) will be accepted. Due to the number of events submitted, space on the print calendar may be limited so we will prioritize events in the month of May from current Stanford groups and departments. All other events will be included on the online comprehensive calendar. If you have any questions, please connect with us over email.
Following a Congressional Resolution in 1978, Asian American Heritage Week was celebrated during the first 10 days of May. This timing was chosen because two important anniversaries fall during this period: the arrival of the first Japanese immigrants in the United States on May 7, 1843, and the completion of the transcontinental railroad by many Chinese laborers on May 10, 1869.
In 1990, President George H. W. Bush and Congress voted to expand the celebration, and since 1992, May has been designated as AAPI Heritage Month. In 2021, President Joe Biden expanded the official proclamation of the month to AANHPI (Asian American, Native Hawaiian, and Pacific Islander) Heritage Month.
At Stanford, the Asian American Activities Center (A3C), Native American Cultural Center (NACC), Asian American Studies Program, Okada House, Pacific Islander Student Association (PISA), and the Asian American Students' Association (AASA) intentionally adopt the term ‘Pasifika’ in renaming the month to "Asian American & Pasifika Heritage Month" to recognize and honor the unique histories, culture, and political movements of Indigenous Island communities that are often silenced through aggregate terms like ‘Asian American’ or ‘Asian & Pacific Islander.’
Each year, Asian American & Pasifika Heritage Month is celebrated with community festivals, government-sponsored activities, and educational activities for students.
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Asian American Awards Ceremony
By providing a space for students to grapple with thorny questions about identity, race and class, the A³C and its fellow community centers are able to develop leaders with a more mature sophisticated understanding of how to work with diverse communities
DANIEL '10
The Stanford Asian American Awards was created in 1999 by the Asian American Activities Center (A³C) Advisory Board, which has served as an advocacy board to the Asian, Asian American and larger Stanford communities. Since its inception, the board has consistently promoted a greater awareness of Asian American students at Stanford and has assisted A³C in serving students more effectively. This award ceremony gathers together members of the Stanford Asian American community and recognizes individuals for their tremendous service, achievement and dedication.
The recipients of the Stanford Asian American Awards are selected from a wide range of individuals throughout the Stanford community. Categories for each sector of the community have been established to fully recognize those who have demonstrated outstanding dedication and service to the Asian American community at Stanford and beyond.
The Stanford Asian American Awards Gala honors faculty, staff, alumni and undergraduate and graduate students for their outstanding achievements and service. The gala is an opportunity for all segments of the community to come together, renew ties, and look forward to new collaborations and projects.
2025 Stanford Asian American Awards
Undergraduate Awards
- Cindy Ng Special Achievement Award
- Community Building Award
- Gender/Sexuality Issues Award
- Performing/Fine Arts Award
- Public Service Award
- Research Award
Graduate Awards
- Leadership Award
- VPGE Research Award
Faculty Award
- Faculty Award
Staff Award
- Asian Staff Forum (ASF) Award
- Filipino American Community at Stanford (FACS) Award
Alumni Award
- Alumni Award
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Julianna Keipp ‘23
I helped to conduct outreach at Asian American Drug Abuse Program (AADAP)'s Safe Syringe Program sites, where people who inject drugs are able to safely dispose of their used syringes with us and receive free sterile syringes in exchange. This is part of numerous harm reduction services I helped facilitate, along with handing out life-saving Naloxone and hygiene kits to unhoused populations. After conducting outreach, I uploaded relevant paperwork into online databases for organizational needs and funding from the county.
I was definitely able to be exposed to the field of work I have been attracted to idealistically, but had never seen in practice. I found myself able to translate concepts I had learned about in my courses into physical settings that helped real clients, which was exciting. However, there was a tricky balance to strike where I had to do this without tokenizing the clients themselves. In coursework, I tend to see individuals as pieces in a bigger theory.
But in practice, I had to see them as individual people with their own lives and struggles. I gained more direction as to what work I want to do in the future and which populations I'd like to focus on in my career.
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Kevin Thor '24
One of the most significant contributions I made to my host organization was developing their post-program survey/interviews for their youth summer program - Building Leaders Organizing Our Movement (BLOOM), and conducting research about grants that could possibly help fund VROC and their mission. The VROC staff/team expressed their gratitude towards me for being able to take on the post-program survey/conduction of interviews for their youth summer program, as they were prepping for their 10-year anniversary Gala.
Being able to take this project on towards the end of my internship was extremely rewarding, because I was able to:
1) talk to people and
2) being able to hear so much positivity and "glows" about the program and how it not only personally shaped the participants' own growth, but increased their awareness about what was happening in the political landscape of Orange County.
I created a report in which VROC can utilize to display to donors the impact BLOOM has on youth, and how their stories can be translated into grant funding. As mentioned previously, being able to research which grants VROC could possibly apply to was huge, as VROC was having some difficulty finding grants to apply to/foundations that supported their work. It definitely was quite difficult finding which grants/foundations aligned and supported VROC's work (given the intersecting identities of queerness and being Asian (broadly speaking), but I was able to find quite a few, such as the Asian American Foundation, the Gill Foundation, and the Rainbow Endowment, just to name a few. Not only did I feel like I made a huge contribution to my host organization through grant and foundation(s) research, but I saw and felt myself growing and learning, which I believe to be a significant contribution to myself.
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CAPS at the A³C
"My career trajectory after Stanford was heavily influenced by my work at the A³C. I focused on social justice work – as an attorney at the ACLU and Associate Director of the City of San Francisco Department on the Status of Women."
ANU '99
Meet Helen Hsu, Psy.D.
Dr. Helen Hsu (she/her/他) is Director of Outreach for Stanford CAPS. She was born in Taipei, Taiwan and grew up all around the Bay Area. She is a proud working class, non-traditional student, (alternative high school to community college to UC route), a past president of the Asian American Psychological Association, past President of the American Psychological Association (APA) Div. 45 (Society for the study of Culture, Race, and Ethnicity) and a representative for the APA Committee on Sexual Orientation and Gender Expression. She spent most of her career in community-based BIPOC focused clinics before joining Stanford as liasion to A3C. She wrote the Healing Trauma Workbook for Asian Americans which was published in 2024.
Meet Christine Catipon, Psy.D.
Dr. Christine Catipon (she/they/siya) is a female, queer, first-generation Filipinx American licensed clinical psychologist. She is the current President of the Asian American Psychological Association and is a frequent speaker on mental health in AANHPI, LGBTQ+, and Filipina/x/o communities. Her clinical interests include anxiety and mood disorders, college mental health, first generation students of color, LGBTQ+ community, AANHPI mental health and social justice, Filipina/x/o identity, life transitions and adjustment, spirituality, and outreach/community engagement. In her free time, she loves playing mahjong, singing karaoke, and exploring the Filipino food scene in the Bay Area.
Meet Ang Li, LMFT
Ang Li (he/him) is a male Chinese American licensed marriage and family therapist who provides therapy in both Mandarin and English. He has worked in a variety of settings from high school counseling to community mental health to psychiatric hospital. Ang Li is currently working as a clinician at CAPS. His clinical interests are working with people who suffer from depression, anxiety, OCD, and a variety of other disorders, particularly those who came from difficult family dynamics or are still struggling with difficult familial or relationship issues. In his free time, he loves playing good videogames (i.e. Black Myth Wukong, BOTW, TOTK, etc.) and having a good time with his partner.
CAPS Counselors in our Community
We are fortunate to have a team of clinicians who self-identify as Asian American and can provide a culturally-sensitive approach to the diversity of our community. Please check out the CAPS website for more information about their team: CAPS Staff Profiles.
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Aeronautics and Astronautics
Education
Research
Anton Ermakov’s fascination with planets and moons
Ermakov combines planetary science and exploration to learn new – and often surprising – details about the structure and evolution of planetary bodies.
‘Nanotechnology is everywhere’: Why very small tech matters
As nano@stanford prepares to open its doors to visitors, nanoscientist Debbie Senesky shares her take on what nanotech can do, on Earth and in space.
Academics
Why Aero/Astro at Stanford?
With a history rich with six Guggenheim Medal recipients and 14 living members of the National Academy of Engineering, our faculty and emeriti faculty continue to be among the most highly decorated in the world. However, it is because of our amazingly talented students that we continue to be ranked among the top of all aerospace engineering departments in the nation.
Our People
Research Areas
Stanford’s Aeronautics and Astronautics department addresses a broad range of technologies to develop systems, design, and analysis methods to further our nation’s aerospace enterprise and improve society.
Welcome
Welcome to the Department of Aeronautics & Astronautics at Stanford University. Our dual mission focuses on the education of future aerospace engineering leaders and on aerospace research with societal impact. We are proud of our culture of innovation and entrepreneurship and the exceptional achievements of our students, alumni, and faculty in industry, government, and academia.
Professor and Chair, Juan J. Alonso
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Coterm Admissions
Stanford undergraduates may work simultaneously toward a bachelor’s and a master’s degree. The degrees may be granted simultaneously or the bachelor’s degree may be awarded first.
Stanford undergraduates who wish to continue their studies for the Master of Science degree in the coterminal program must have a declared major and have earned a minimum of 120 units towards graduation. This includes allowable advanced placement (AP) and transfer credit. For university coterminal degree program rules, visit the coterm section of the Registar’s website.
Application Deadlines
Proposed MS start quarter: Application deadline
A completed application (including letters of recommendation and transcripts) must be received by the deadline. You may not apply later than the quarter prior to the one in which you expect to finish your undergraduate degree. Stanford undergraduates with any major who are interested in learning more about receiving an Aero/Astro master's degree as a coterm should review the information on the University Registrar’s website and visit the Aero/Astro Student Services Office. A coterm application fee of $125 will be assessed for each coterm application. This fee will be added to your University Bill, should you be admitted to the program and choose to accept the offer of admission.
Coterm Application Checklist
Application and fee ($125)
- Statement of purpose
- Letters of recommendation
- Unofficial transcript
- MS proposed program proposal
Application Fee
Students who accept an offer of admission and are matriculated (enrolled) into a coterm graduate program are assessed a $125 coterm application fee. Eligibility for a co-term graduate application fee waiver is based on the Financial Aid Office’s evaluation of your need-based aid application. Those from families with income below $125,000 and typical assets for that income range will qualify. The waiver is automatically applied and no special request is necessary.
Statement of Purpose
Your statement of purpose should identify personal and professional goals. It should also discuss your development to date and your intentions relative to graduate study and life beyond Stanford. The Aero/Astro Graduate Admissions Committee reads your statement of purpose with interest because, along with the letters of recommendation, it offers insight into who you are as an individual. Your statement of purpose should not exceed two pages in length (single spaced).
Letters of Recommendation
Two Stanford faculty letters of recommendation are required. Please see the online application for information.
Transcripts
Please upload one unofficial transcript to your application, or you may request and submit an official electronic transcript through the online application.
Application Decisions
Applicants will be notified of the admission decision in writing, typically 4 weeks after the application deadline.
If you are accepted into the program, you will need to:
- Submit a response to the offer of admission to Aero/Astro Student Services Office, and correspond with a member of the student services staff to discuss the coterm plan and complete paperwork.
- Submit a new AA-MS program proposal, signed by your advisor, by the Final Study List deadline of your first co-term quarter. An academic advisor will be assigned upon admission.
New coterm students should be sure that all paperwork is completed in a timely fashion to ensure the appropriate transfer of undergraduate coursework toward the MS program. More information is on the University Registrar’s website. After accepting admission to this coterminal master’s degree program, students may request transfer of courses from the undergraduate to the graduate career to satisfy requirements for the master’s degree. Transfer of courses to the graduate career requires review and approval of both the undergraduate and graduate programs on a case by case basis. Courses taken during or after the first quarter of the sophomore year are eligible for consideration for transfer to the graduate career; the timing of the first graduate quarter is not a factor. No courses taken prior to the first quarter of the sophomore year may be used to meet master’s degree requirements. Course transfers are not possible after the bachelor’s degree has been conferred.
Financial Aid
Fellowships: See the university financial aid website. The School of Engineering Dean’s office has a pilot coterminal fellowship program to which students can apply after admission to the coterm. Standard Stanford Fellowships are generally not awarded to coterm applicants. We recommend that you apply for external fellowships to fund your graduate program.
Research Assistantships: Aero/Astro research assistantships (RAs) are usually considered part of a long-term commitment to doctoral-level research, so it is rare for an incoming student to receive an RA offer in this department. The research assistants are selected by individual faculty members, who will usually have worked with the student in one or more courses, and in some directed study, before deciding on an RA appointment.
Course Assistantships: Application information for course assistantships (CAs) will be posted by the Aero/Astro Student Services Office in the spring quarter. Positions are assigned by the department quarterly, beginning in the summer for the following academic year. Applicants are expected to have taken and done well in the course in which they will assist.
Questions?
Email: aa-admissions@stanford.edu
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PhD Admissions
The Doctor of Philosophy (PhD) degree is intended primarily for students who desire a career in research, advanced development, or teaching. Students in the PhD program obtain a broad education in the core areas of Aeronautics and Astronautics through coursework, while also engaging in intensive research in a specialized area, culminating in a doctoral thesis.
An MS degree is not required to apply to the PhD program in Aeronautics and Astronautics. Students with a Bachelor’s degree who ultimately intend to complete a PhD degree are strongly encouraged to apply directly to the PhD program, rather than the MS program.
Current Stanford MS students interested in adding a PhD program to their academic career should speak with the staff at the Aero/Astro Student Services Office about the necessary paperwork and relevant policies. If you are a current master's student in the Stanford Aeronautics and Astronautics Department, to apply for the PhD, you must complete paperwork prior to conferring the MS degree.
Application Deadlines
We have one PhD admission cycle. Application deadlines are final. A completed application (including letters of recommendation, transcripts and TOEFL scores) must be uploaded by the deadline. Applications will NOT be accepted after the deadline. A completed application (including letters of recommendation, transcripts and TOEFL scores) must be received by the following date:
Autumn 2025-26: December 3, 2024
Application Requirements
To be eligible for admission to the PhD program, applicants must either:
- hold, or expect to hold before enrollment at Stanford, a bachelor’s degree from a U.S. college or university.
- Applicants from institutions outside the U.S. must hold the equivalent of a U.S. bachelor’s degree from a college or university of recognized standing. See minimum level of study required of international applicants.
Students who meet the above degree requirement with a strong technical background in engineering, physics, or a comparable science program are welcome to apply; a bachelor's degree in aeronautics and astronautics or mechanical engineering is not strictly required.
All students interested in pursuing a PhD in Aeronautics and Astronautics should use the Stanford Graduate Admissions Application. Your application must include all of the materials listed below and be received by Stanford by the application deadline. The fee for online graduate applications is $125.
Required Application Documents
- Online Application
- Application Fee
- Statement of Purpose
- 3 Letters of Recommendation
- Official TOEFL* Scores, if applicable
Application Fee Waiver
If you are considering Stanford graduate programs and need assistance with the application fees, consider applying for a fee waiver.
Statement of Purpose
Your statement of purpose should identify personal and professional goals. It should also discuss your development to date and your intentions relative to graduate study and life beyond Stanford. The Aero/Astro Graduate Admissions Committee reads your statement of purpose with interest because, along with the letters of recommendation, it offers insight into who you are as an individual. Your statement of purpose should not exceed two pages (single-spaced).
Transcripts
Submitting transcripts when you are applying, and after you have been offered admission are two separate steps. When applying: You must upload one scanned version of your transcript(s) in the online application. Please read the Applying section of this website for important information about submitting transcripts. If offered admission: Please see this page for information on submitting final official transcripts.
Letters of Recommendation
Three letters of recommendation are required; one letter must come from an academic source, although at least two are preferred. Recommendations must be submitted online. Please see the "Recommendations" section of the online application for information. Please do not submit letters of recommendation through Interfolio.
TOEFL Scores
Adequate command of spoken and written English is required for admission. Applicants whose first language is not English must submit an official test score from the Test of English as a Foreign Language (TOEFL). Stanford accepts only ETS (Educational Testing Service) scores. TOEFL results must be from an examination taken within the past two years. The Stanford institution code for ETS reporting is 4704. You do not need a department code. For more information on TOEFL requirements, please see the Required Exams and Frequently Asked Questions sections on the Graduate Admissions website.
*Stanford will temporarily accept the TOEFL ITP Plus test with the Vericant interview for applicants from Mainland China who are unable to sit for the TOEFL iBT. This exception is requested only for the 2020-2021 application cycle. Applicants may be asked to re-test at a later time once the Stanford TOEFL iBT becomes available, or applicants may be asked to re-test through the Stanford Language Center. Per current University policy, all international students including those from Mainland China must receive English language clearance from the English for Foreign Students program prior to becoming a teaching assistant.
Exemptions are granted to applicants who have earned (or will earn, before enrolling at Stanford) a U.S. bachelor’s, master’s or doctoral degree from a college or university accredited by a regional accrediting association in the United States, or the international equivalent degree from a university of recognized standing in a country in which all instruction is provided in English. U.S. citizenship does not automatically exempt an applicant from taking the TOEFL if the applicant’s first language is not English.
Reapplying
Reapplicants must submit new supporting documents and complete the online application as outlined above, in the graduate application checklist. Only prior official test scores can be reactivated.
Application Status
You may view your application status and decision by logging into your status page. Due to the volume of applications we receive, we are not able to confirm with individual applicants when documents have been received. All applicants should monitor the online checklist to track individual documents. It is the applicant's responsibility to monitor the checklist and ensure that all documents are received by the deadline.
Admission Decisions
Completed applications are reviewed by the faculty Admissions Committee throughout the winter. A select group of applicants will be interviewed during the evaluation process. Letters are sent as decisions are made, beginning in March. The selection of graduate students admitted to the Department of Aeronautics and Astronautics is based on an individualized, holistic review of each application, including (but not limited to) the applicant’s academic record, the letters of recommendation, the statement of purpose, personal qualities and characteristics, and past accomplishments.
PhD Funding
All SoE PhD students who are in good standing relative to their PhD program requirements will be funded to the department’s PhD standard. In all departments, this is at least equivalent to Stanford’s 20-hour-RA salary plus tuition to cover the department’s required enrollment (summer enrollment requirements vary by department). Funding can include fellowships, research assistantships, training grants and teaching assistantships. PhD students are encouraged to pursue outside fellowships. Besides the prestige, fellowships give the recipient greater flexibility in determining their own research direction.
Knight-Hennessy Scholars
Join dozens of Stanford Engineering students who gain valuable leadership skills in a multidisciplinary, multicultural community as Knight-Hennessy Scholars (KHS).
KHS admits up to 100 applicants each year from across Stanford’s seven graduate schools, and delivers engaging experiences that prepare them to be visionary, courageous, and collaborative leaders ready to address complex global challenges. As a scholar, you join a distinguished cohort, participate in up to three years of leadership program, and receive full funding for up to three years of your PhD studies at Stanford.
Candidates of any country may apply. KHS applicants must have earned their first undergraduate degree within the last seven years, and must apply to both a Stanford graduate program and to KHS. Stanford PhD students may also apply to KHS during their first year of PhD enrollment.
If you aspire to be a leader in your field, we invite you to apply. The KHS application deadline is October 9, 2024. Learn more about KHS admission.
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Degree of Engineer — Post MS Program
The Engineer’s degree represents one to two additional years of study beyond the Master’s degree and includes a research thesis. The program is designed for students who wish to do professional engineering work upon graduation and who want to engage in more specialized study than is afforded solely by a Master’s degree. It is expected that full-time students will be able to complete the degree within two years of study after the master’s degree.
Applicants not currently enrolled at Stanford should follow the standard procedures for graduate applications. Current Stanford students who wish to continue or transfer to the Engineer’s degree program should submit the following materials to the Aero/Astro Student Services Office several weeks before the end of their current degree program:
- Online Graduate Program Authorization Petition;
- Statement of purpose describing the area of study and topic for thesis research;
- Short letter from an Aero/Astro faculty member, addressing your preparation for the proposed research and their willingness to serve as your academic/research advisor.
Refer to the “Changes of Degree Level or Department” section, above, for more information.
Course requirements
Each individual Engineer’s Degree program, designed by the student in consultation with the advisor, should represent a strong and cohesive program reflecting the student's major field of interest. Engineer’s Degree candidates must complete a minimum of 90 units. Candidates who received their M.S. from Stanford may count up to 45 units towards the 90-unit total. Students who received an MS degree at another institution may petition (through the university Registrar’s Office) to transfer up to 45 units toward the 90-unit requirement.
Of the 45 units required beyond the M.S., a student must complete a minimum of 21 units (including 6 units of mathematics) of approved courses in advanced study in engineering, science, and mathematics (excluding research, directed study, and seminars) beyond the MS degree. These units must be taken for a letter grade, and all courses must be numbered 200 and above. Note: One math course may be taken at the 100 level if approved by the advisor. Students may register for up to 24 units of Engineer thesis. Units which were applied toward the MS degree cannot be used again. An advisor approved Engineer’s Degree course proposal must be submitted when applying for Engineer’s Degree candidacy.
Mathematics Courses: Engineer’s Degree candidates are expected to exhibit competence in applied mathematics. Students meet this requirement by taking two courses - a minimum of six units – of either advanced mathematics offered by the Mathematics department or courses that strongly emphasize methods of applied mathematics. The Aero/Astro Department and the other engineering departments offer many courses that have sufficient mathematical content that they may be used to satisfy the mathematics requirement; a pre-approved list is included in this Guide, but there are many others which may be acceptable. Please consult with your advisor and the Aero/Astro Student Services Office before assuming that a particular course will be accepted in your own program. Note: One math course may be taken at the 100 level if approved by the advisor.
Academic Requirements
Every student should be familiar with the University’s requirements for minimal progress as outlined in the Graduate Academic Policies and Procedures GAP. A minimum cumulative grade point average (GPA) of 3.0 is required to fulfill the department’s Engineer’s Degree, and to maintain satisfactory academic standing in the program. It is incumbent upon the student to request letter grades in all courses listed on the Application for Candidacy form. Students must receive a passing grade, and maintain a minimum GPA of 3.0, on all courses listed on the Candidacy form.
Candidacy
Students in the Engineer degree program must submit an Application for Candidacy no later than the second quarter of Engineer's study. This form indicates the courses and thesis work which the student will be using for the degree. If the research topic cannot be clearly described when this form is filed, the area of research should be described along with a timetable for identifying a thesis topic. Aero/Astro has a department-specific Candidacy form, available in the Aero/Astro Student Services Office.
The Application for Candidacy should be signed by the student's research advisor, and submitted to the Aero/Astro Student Services Office for the Director of Graduate Studies’ approval signature. Neglecting to file for candidacy can prevent you from receiving your degree. Changes to your program of study can be filed at any time by submitting a revised Candidacy form. Obtain your advisor's signature and submit it to the Aero/Astro Student Services Office for Director of Graduate Studies’ signature. In order to graduate or go TGR, you must have completed all the units listed on your current Candidacy form.
Engineer’s thesis
For specific information regarding the format and deadlines for submission of theses, please check with the Graduate Degree Progress Office. The department recommends that students follow the format defined in the Format Requirements for Your Thesis. Note: the advisor must sign the thesis before the filing deadline, which is generally the last day of classes during the graduation quarter.
Mid-year degrees are not officially conferred until the first week of the quarter after degree completion, and actual diplomas are distributed at the University's Commencement in June. However, students who have submitted their theses and have no outstanding Stanford obligations (financial or academic) may obtain an official University "certificate of completion" from the Graduate Degree Progress Office after degree conferral.
Degree completion
Candidates for the engineer degree are required to have a minimum GPA of 3.0 for courses beyond those required for the master's degree. All courses except seminars and those that are mandatory pass/no-credit should be taken for a grade. Students must also meet the university’s quarterly academic requirements for graduate students, as described in the Bulletin and in the Satisfactory Progress section of the Guide to Graduate Studies in Aeronautics and Astronautics.
For midyear degrees, the date of conferral is early the quarter after degree completion. Students who have no outstanding Stanford obligations (financial or academic) may obtain an official “certificate of completion” from the Graduate Degree Progress Office at any time after finishing. Diplomas are printed only once a year, for distribution at Commencement in June.
Once students have begun study for the Engineer Degree, they have five years to complete the program. This time is not extended by leaves of absence.
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Master’s Program
Our Master of Science program is based on the completion of lecture courses focused on a theme within the discipline of Aeronautics and Astronautics engineering. No thesis is required. No research is required.
The master’s degree program requires 45 quarter units of course work, which must be taken at Stanford. The course work is divided into four categories: basic courses, mathematics courses, technical electives and other electives.
Basic courses
MS candidates must select eight courses as follows:
Five courses in the basic areas of Aeronautics and Astronautics (one in each area)
- Fluids: 200 (Applied Aerodynamics), 210A (Fundamentals of Compressible Flow)
- Structures: 240 (Analysis of Structures)
- Guidance and Control: ENGR 105 (Feedback Control Design), ENGR 205 (Introduction to Control Design Techniques)
- Propulsion: 283 (Aircraft and Rocket Propulsion), 204 (Spacecraft Electric Propulsion)
- Experimentation/Design Requirements (PDF)
A maximum of six independent study/research units (AA 290 or independent study in another department) may count toward your MS program. If you fulfill your Experimentation/Design requirement with a course other than AA290 (or equivalent from another department), it is possible to count AA 290(s) in the technical or free elective sections.
Three courses, one each from three of the four areas below
- Fluids: 200 (Applied Aerodynamics), 210A (Fundamentals of Compressible Flow), or 244A (Introduction to Plasma Physics and Engineering)
- Structures: 242B (Mechanical Vibrations), 256 (Mechanics of Composites), 257 (Structural Health Monitoring), or 280 (Smart Structures)
- Guidance, Navigation, Dynamics, and Control: 242A (Classical Dynamics), 242B (Mechanical Vibrations), 251 (Introduction to the Space Environment), 271A (Dynamics and Control of Spacecraft and Aircraft), 272 (Global Positioning Systems), 274A (Principles of Robot Autonomy I), 275 (Navigation for Autonomous Systems), 277 (Multi-robot Control and Distributed Optimization), or 279A (Space Mechanics)
- One course selected from A/A courses numbered 200 and above, excluding seminars and independent research.
Students who believe they have satisfied Basic Course requirements previously may request a waiver of one or more courses. (See “Waivers and Transfer Credits” below.)
Mathematics courses
M.S. candidates are expected to exhibit competence in applied mathematics. Students meet this requirement by taking two courses - a minimum of six units – of either advanced mathematics offered by the Mathematics department or technical electives which strongly emphasize methods of applied mathematics. Approved mathematics courses offered by the Aero/Astro department include:
- AA203 - Optimal and Learning-Based Control
- AA212 - Advanced Feedback Control Design
- AA214 - Numerical Methods for Compressible Flows
- AA218 - Introduction to Symmetry Analysis
- AA222 - Engineering Design Optimization
- AA228/CS 238 - Decision Making under Uncertainty
- AA242B - Mechanical Vibrations
- AA273 - State Estimation and Filtering for Robotic Perception
- AA277 - Multi-Robot Control and Distributed Optimization
The list of mathematics courses in the department's handbook (PDF) has additional suggestions, and includes all courses in mathematics numbered 200 or above. In order to use applied mathematics courses not on either list to fulfill this requirement, prior approval should be obtained from the student's advisor and the candidacy chair. (Note: Calculus, ordinary differential equations and vector analysis are fundamental math prerequisites and will not satisfy the mathematics requirement. They may be counted only as a free elective.)
A maximum of six independent study/research units (AA 290 or independent study in another department) may count toward your MS program. If you fulfill your Experimentation/Design requirement with a course other than AA290 (or equivalent from another department), it is possible to count AA 290(s) in the technical or free elective sections.
Technical electives
Students, in consultation with their advisor, will select at least four courses* from among the graduate-level courses, totaling at least 12 units, from departments in the School of Engineering and related science departments. This requirement increases by one course [3 units] for each basic course requirement which is waived. Up to three seminar units may count toward a technical elective requirement (equivalent to one technical elective course). These courses should be taken for a letter grade; the student should not elect the credit/no credit option for any course except free elective.
*Up to three seminar units may count toward your MS program, and will be counted as one technical elective. At least three additional graduate courses offered in Engineering or related math/science departments should be taken to meet the technical elective section requirement.
Other electives
It is recommended that all candidates enroll in a humanities or social sciences course to complete the 45-unit requirement. Courses fulfilling this requirement may be taken as credit/no credit. Practicing courses in art, music, dance and physical education do not qualify in the free elective section. With advisor approval, language courses will qualify.
Waivers and transfers of credit
Waivers of the Basic Courses required in the M.S. program can only be granted by the instructor of that course. Students who believe that they have had a substantially equivalent course at another institution should consult with the course instructor to determine if they are eligible for a waiver, and with their advisor to judge the effect on their overall program plans. To officially request a waiver, students should fill out a Petition for Waiver form (reverse side of the department's Program Proposal) and have it approved by the instructor and their advisor. One additional technical elective must be added for each Basic Course which is waived.
Students taking Aero/Astro qualifying exams are strongly encouraged to take specific classes, and there is a separate waiver process for Qualifying Examination core courses (described in “Ph.D. Qualifying Procedures in Aero/Astro”). Students should consult with their advisor before waiving courses.
Program proposal for master’s degree
Each master’s student must submit a master’s program proposal by the last day of classes in the first quarter of study. It must be signed by the advisor, then submitted to the Aero/Astro Student Services Office for the candidacy chair’s approval signature. This first submission is intended as a planning document to ensure that the student has identified at least one plan of study that meets all department and university requirements and that also fits the student's own abilities and interests. Recommended timing: the student should discuss several versions of this overall plan with the advisor when choosing classes for the first quarter; then schedule an appointment for just after midterms to work out a detailed course plan for future quarters and file an official program proposal for department review and approval.
Any changes to the program of study should be made in consultation with the advisor and the student services administrator. Such changes may be made more than once, but the final program proposal must be filed early in the quarter in which the degree is to be conferred. The changed program of study should be summarized on a program proposal marked “Revised,” signed by the advisor and submitted to the Aero/Astro Student Services Office for the Director of Graduate Studies' approval signature. The MS degree cannot be conferred unless the student has successfully completed all courses on the most recent, fully approved program proposal.
International students should consult with the Bechtel International Center, but typically must be enrolled in at least 8 units per quarter to maintain legal status.
Research opportunities
No thesis is required, but there is an opportunity for students to become involved in research projects during their master’s year(s). Students interested in this opportunity should make arrangements with a faculty member to supervise their research and enroll in AA290 to receive academic credit. Students can count up to 6 units of research towards their MS degree.
Coterminal master's program
This program allows Stanford undergraduates an opportunity to work simultaneously toward a B.S. degree and an M.S. in Aeronautics and Astronautics. Stanford undergraduates who wish to continue their studies for the master of science degree in the coterminal program must have earned a minimum of 120 units towards graduation. This includes allowable Advanced Placement (AP) and transfer credit.
The department-specific Aero/Astro coterminal program application, which includes information and deadlines, can be obtained from the Aero/Astro Student Services Office. A completed application (including letters of recommendation and transcripts) must be received no later than the quarter prior to the expected completion of the undergraduate degree. Admission is granted or denied through the departmental faculty admissions committee. Stanford undergraduates interested in learning more about receiving an Aero/Astro master's degree as a coterm student should review the information on the University Registrar's web site and visit the Aero/Astro Student Services Office.
University Coterminal Requirements
Coterminal master’s degree candidates are expected to complete all master’s degree requirements as described in this bulletin. University requirements for the coterminal master’s degree are described in the "Coterminal Master's Degrees" section. University requirements for the master’s degree are described in the "Graduate Degrees" section of this bulletin.
After accepting admission to this coterminal master’s degree program, students may request transfer of courses from the undergraduate to the graduate career to satisfy requirements for the master’s degree. Transfer of courses to the graduate career requires review and approval of both the undergraduate and graduate programs on a case by case basis.
In this master’s program, courses taken during or after the first quarter of the sophomore year are eligible for consideration for transfer to the graduate career; the timing of the first graduate quarter is not a factor. No courses taken prior to the first quarter of the sophomore year may be used to meet master’s degree requirements.
Course transfers are not possible after the bachelor’s degree has been conferred.
The University requires that the graduate advisor be assigned in the student’s first graduate quarter even though the undergraduate career may still be open. The University also requires that the Master’s Degree Program Proposal be completed by the student and approved by the department by the end of the student’s first graduate quarter.
Part-time master’s degree program
Only applicants for the part-time master’s degree program for working professionals (HCP) are considered quarterly. Prospective HCP students follow the same admissions process and must meet the same admissions requirements as full-time graduate students.
Honors cooperative program
The Honors Cooperative Program (HCP) makes it possible for academically qualified engineers and scientists in nearby companies to be part-time master's students in Aeronautics and Astronautics while continuing nearly full-time professional employment. For more information regarding the Honors Cooperative Program, see the Stanford Center for Professional Development web site.
Degree completion
Every student should be familiar with the University’s requirements for minimal progress as outlined in the Graduate Academic Policies and Procedures GAP. A minimum grade point average (GPA) of 2.75 is required to fulfill the department's Master’s degree requirements. Students must also meet the University’s quarterly academic requirements for graduate students, as described in the Bulletin. All units must be in courses at or above the 100 level, and all courses used to satisfy the Basic Courses, Mathematics and Technical Electives requirements must be taken for a letter grade (excluding seminars).
For midyear degrees, the date of conferral is during the first week of the next quarter. Students who have no outstanding Stanford obligations (financial or academic) may obtain an official "certificate of completion" from the Graduate Degree Progress Office after degree conferral. Diplomas are distributed once a year at Commencement in June. In addition, diplomas for graduate degrees are available for pickup or by mail. Once you have begun study for the Master’s, you have three years to complete the degree (five years for Honors Cooperative students). This time is not extended by Leaves of Absence.
Time limits for M.S. Degree:
- HCP (Honors Cooperative Students): Five years from the first quarter of enrollment in the MS program.
- Co-terminal students: Three years after the quarter in which 180 units are completed.
- All other students: Three years from the first quarter of enrollment in the M.S. program, or 60 units completed.
Study after the master’s degree
Students wishing to continue at Stanford after receiving the MS degree must be approved for further study during their final master's quarter. (This includes approval for the Engineer's degree or PhD in Aero/Astro, or for a degree in another department.) In order to stay here, a graduate program authorization form should be fully approved and filed at the Registrar’s Office before the MS is conferred.
Students who are not citizens or permanent residents of the U.S. will need to verify their funding for the new degree and update their visa documentation as part of the Graduate Program Authorization procedure. Appropriate forms are available from the Bechtel International Center. Support from research assistantships can be verified by the professor providing support, and the Student Services Office can verify course assistantships. For personal funding or other support, inquire at the Bechtel International Center about the proper source for verification.
Students (admitted for Autumn 2021-22) wishing to continue to the Ph.D. program
The M.S. program is designed for students who intend to proceed directly to a professional career in aeronautics and astronautics. However, in rare cases, an M.S. student may be able to transition into the Ph.D. program. To do so, the student must have commitment from a professor willing to fund and advise the student throughout their Ph.D. Such students may submit an application for the Ph.D. program in the Autumn Quarter (mid-October deadline) of their second year in the M.S. program.
Required Application Material
- Statement of purpose describing the area of study and topic for thesis research
- Letter of Recommendation from Ph.D. advisor
- If the proposed Ph.D. advisor is not in the Aero/Astro Department, consult with the Aero/Astro Student Services Office in advance to ensure eligibility
- Confirmation of funding form signed by Ph.D. Advisor
Note: Students must be in good standing, and eligible to take the departmental qualifying examination (quals) in Spring Quarter of year two (see Ph.D. Qualifying Procedures in Aero/Astro, below).
The Admissions Committee will review the required application material, including previous application and current Stanford transcript, and make admission decisions by the end of the Autumn Quarter. If a student is approved for the Ph.D. program, a Graduate Program Authorization petition ($125 fee) must be submitted online through AXESS and fully approved before the M.S. is conferred (see Changes of Degree, above). Enrollment as a Ph.D. student, including funding, will begin in Winter Quarter of year two.
Students who are not citizens or permanent residents of the U.S. will need to verify their funding for the new degree, and update their visa documentation, as part of the Graduate Program Authorization procedure. These forms are available from the Bechtel International Center. Support from research assistantships can be verified by the professor providing support and the Aero/Astro Student Services Office.
If you leave Stanford for employment or study at another institution and later wish to return for further degree work, you will need to submit a standard admission application to the department. Check with the Aero/Astro Student Services Office to verify deadlines and required credentials. International students will be subject to visa requirements when they are considered for admission.
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2012 Affiliates Meeting
Tuesday, April 24, 2012
Presenters
Design and Flight Test of a Cable Angle Feedback Control System for Helicopters with External Loads
Christina Ivler
Abstract
Slides
Advisor: Prof. David Powell
Hybrid Propulsion for Solar-System Exploration
Ashley Chandler
Abstract
Slides
Advisor: Prof. Brian Cantwell
Modeling Plasma for Flow Control
Amrita Lonkar
Abstract
Slides
Advisor: Prof. Juan Alonso
Supersonic Low-Boom Design in the NASA N 2 Project
Trent Lukaczyk
Abstract
Slides
Advisor: Prof. Juan Alonso
Large-Scale Shock Unsteadiness in Over-Expanded Nozzles
Britton Olson
Abstract
Slides
Advisor: Prof. Sanjiva Lele
Airframe Noise Prediction Using Large Eddy Simulation
Chris Yu
Abstract
Slides
Advisor: Prof. Sanjiva Lele
Robust Terrain-Relative Navigation of Underwater Vehicles
Sarah Houts
Abstract
Slides
Advisor: Prof. Steve Rock
Robust Sensing for Autonomous Rendezvous and Docking
José Padial
Abstract
Slides
Advisor: Prof. Steve Rock
Independent Ephemerides for Fault-Free Air Navigation Using GPS
Tyler Reid
Abstract
Slides
Advisor: Prof. Per Enge
Multifunctional Analysis for the Design of Intelligent Composites
Kuldeep Lonkar
Abstract
Slides
Advisor: Prof. Fu-Kuo Chang
High-Order GPU-Based Compressible Fluid Flow Solver for Unstructured Grids
Patrice Castonguay
Abstract
Slides
Advisor: Prof. Antony Jameson
Adjoint-Based Mesh Adaptation
LaLa (Yi) Li
Abstract
Slides
Advisor: Prof. Antony Jameson
High Fidelity Optimization of Flapping Airfoils and Wings
Matthew Culbreth
Abstract
Slides
Advisor: Prof. Antony Jameson
Electrical Failures & RF Anomalies
Theresa Johnson
Abstract
Slides
Advisor: Prof. Sigrid Close
Simulations of Hypervelocity Meteoroid Impacts on Spacecraft
Alex Fletcher
Abstract
Slides
Advisor: Prof. Sigrid Close
A Compressed Sensing Approach to Observing Distributed Radar Targets
Ryan Volz
Abstract
Slides
Advisor: Prof. Sigrid Close
UV-LED Charge Control at 255nm
Karthik Balakrishnan
Abstract
Slides
Advisor: Prof. Dan DeBra
LIGO – Seismic Platform Interferometer
Daniel Clark
Abstract
Slides
Advisor: Prof. Dan DeBra
Here are résumés that students asked to be distributed to affiliates members:
Geoffrey Bower
Ashley Chandler
Rashaad Kazi
Joseph Kenahan
Meir Messingher Lang
Chiawei (Wei) Lee
Nicolas Lee
Lala Yi Li
Ashley Micks
Ron Moore
Brandon Morgan
Robert Nakamura
Melahn Parker
Scott Perry
Louis Poirier
Linda Truong
Kyle Tsai
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2013 Affiliates Meeting
Tuesday, April 30, 2013
Presenters
Visualization of Hybrid Combustion
Elizabeth Jens
Abstract
Slides
Advisor: Prof. Brian Cantwell
Adjoint-Based Methods for Hypersonic Flows in Thermochemical Nonequilibrium
Sean Copeland
Abstract
Slides
Advisor: Prof. Juan Alonso
Supersonic Low-Boom Design in the NASA N 2 Project
Trent Lukaczyk
Abstract
Slides
Advisor: Prof. Juan Alonso
Quadrupole Noise in Turbulent Wake Interaction Problems
Chris Yu
Abstract
Slides
Advisor: Prof. Sanjiva Lele
Numerical Simulations of a Shock Train in a Constant Area Duct Using Wall-Modeled Large Eddy Simulations
Zach Vane
Abstract
Slides
Advisor: Prof. Sanjiva Lele
Ephemeris Message for Future Navigation Augmentation
Tyler Reid
Abstract
Slides
Advisor: Prof. Per Enge
Iceberg-Relative Navigation and Mapping
Marcus Hammond
Abstract
Slides
Advisor: Prof. Steve Rock
Attitude Determination of Passively Magnetically Stablized Nano Satellites
Roland Burton
Abstract
Slides
Advisor: Prof. Steve Rock
Black Box for Satellites
Ashish Goel
Abstract
Slides
Advisor: Prof. Sigrid Close
Turbulence in High Density Plasma Measured by Radar
Jonathan Yee
Abstract
Slides
Advisor: Prof. Sigrid Close
aLIGO Seismic Interferometer
Daniel Clark
Abstract
Slides
Advisor: Prof. Daniel DeBra
High Resolution Thermometry Using High Finesse Optical Cavities
Si Tan
Abstract
Slides
Advisor: Prof. Robert Byer (applied physics)
Faculty Presentation
Future Directions in Space Research: Science Missions, NASA Initiatives, and Commercial Applications
Consulting Prof. Scott Hubbard
Slides
Energy-Stable High-Order Methods for Compressible Viscous Flows
David Williams
Abstract
Slides
Advisor: Prof. Antony Jameson
Heavy Vehicle Viscous Pressure Drag Optimization
David Manosalvas
Abstract
Slides
Advisor: Prof. Antony Jameson
Internally-Activated Rovers for All-Access, Low-Gravity Surface Mobility
Ross Allen
Abstract
Slides
Advisor: Prof. Marco Pavone
Sampling-Based Spacecraft Motion Planning
Joseph Starek
Abstract
Slides
Advisor: Prof. Marco Pavone
Bio-Inspired Sensing Network Design and Development
Zhiqiang Guo
Abstract
Slides
Advisor: Prof. Fu-Kuo Chang
Design of Smart Adhesive Films for Bondline Integrity Monitoring
Yitao Zhuang
Abstract
Slides
Advisor: Prof. Fu-Kuo Chang
Here are résumés that students asked to have distributed to affiliates members:
Dimitri Alves
Aaron Bisely
Harrison Chau
Sean Copeland
Shandor Dektor
Michael Emory
Alex Fickes
Jeffrey Fike
David Gerson
Kazuma Gunning
Sarah Houts
Vaibhav Kumar
Lawrence Leung
Cyrus Liu
Amrita Lonkar
Ashley Micks
Surajit Roy
Devina Sanjaya
Andrew Smith
Paul Tarantino
Ben Tood
Michael Vitus
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2020 Affiliates Meeting
60th Annual Meeting of the Industrial Affiliates of the Stanford Aeronautics & Astronautics Department Virtual Meeting
Wednesday, October 28, 2020
On behalf of the Aeronautics & Astronautics Department, we thank you for your attendance and participation. Here is a link to the video recording from the meeting.
9:00-9:15am State of the Department - Professor Charbel Farhat, Department Chair 9:15-
9:30am Introduction - Professor Marco Pavone, Program Director
9:30-10:00am Lightning Talks from students
● Veronika Korneyeva "Experimental Studies of Flame Propagation in Opposed Flow: Planar Hybrid Fuel Samples and in Hybrid Rocket Geometry" (B. Cantwell)
● Matt Subramanyam “A Universal Profile for Near-Wall Flows” (B. Cantwell)
● Elliot Ransom “Stretchable Sensor Network Smart Skins for IoT Applications" (F. Chang)
● Anthony Bombik "Multifunctional Energy Storage Composites" (F. Chang)
● Ben Estacio "Understanding Electromagnetic Pulse Generation from Hypervelocity Impacts on Spacecraft" (S. Close)
● Trevor Hedges "High-Resolution Plasma Physics Simulation of an Ablating Meteoroid" (S. Close)
● Nathan Stacey "Autonomous Orbit Determination and Asteroid Characterization" (S. D’Amico)
● Matthew Willis "Learning to Fly in Space: Virtual Reality Training for Spacecraft Formation Flight" (S. D’Amico)
● Gabriele Boncoraglio “Breaking the CPU Barrier Between High-Fidelity MDAO and Practical Design” (C. Farhat)
● Jonathan Ho "Towards Seamless MDAO of Aerospace Systems" (C. Farhat)
● Ashwin Kanhere “Quantifying Uncertainty of LiDAR Pose Estimates” (G. Gao)
● Adyasha Mohanty "A Particle Filtering Framework for Integrity Risk of GNSS-Camera Sensor Fusion" (G. Gao)
● Andy Castillo "Kinetic Modelling of Plasma and Gases" (K. Hara)
● Adnan Mansour "Hall Effect Thruster Modelling" (K. Hara)
● Anthony Corso "Validation of Safety-Critical Autonomous Systems Using Machine Learning" (M. Kochenderfer)
● Duncan Eddy "Scheduling At Scale: Automating Operations For Large Satellite Constellations" (M. Kochenderfer)
● Jean de Becdelievre “Improving Bilevel Design Optimization with Flexible Surrogate Modeling” (I. Kroo)
● Loren Newton “Online Training of Neural Networks for Nonlinear Aircraft System Identification.” (I. Kroo)
● Kristen Matsuno “Internal Regulation in Compressible Turbulent Shear Layers” (S. Lele)
● Gary Wu "Why does a supersonic jet sing in harmonic tones?" (S. Lele)
● Karen Leung “How to Expect the Unexpected: Ensuring Safety in Human-robot Interactions” (M. Pavone)
● Apoorva Sharma "Safe Learning-Enabled Autonomy" (M. Pavone)
● Adam Wiktor "Cooperative Multi-Robot Localization in Natural Terrain" (S. Rock)
● Aditya Mahajan "Autonomous Exploration of Complex Environments using Active SLAM" (S. Rock)
● Adam Caccavale "MSL-RAPTOR: A 6DoF Relative Pose Tracker for Onboard Robotic Perception" (M. Schwager)
● Kunal Shah "Aerial surveys with teams of drones in extreme environments: A case study on Antarctic penguin colonies" (M. Schwager)
● Ruiqi Chen "Mechanical Response of 3D Printed Structures" (D. Senesky)
● Savannah Eisner “Extended Exposure of Gallium Nitride Heterostructure Devices to a Simulated Venus Environment” (D. Senesky)
10:00-10:30pm Breakout Room I (14 parallel tracks, one per student)
10:30-11:00am Breakout Room II (14 parallel tracks, one per student)
11:00-11:15am Break
11:15-12:00pm Affiliate Industry Talks
12:00-1:00pm All-hands discussion with all faculty and affiliates
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2020 Details for Affiliates
Veronika Korneyeva "Experimental Studies of Flame Propagation in Opposed Flow: Planar Hybrid Fuel Samples and in Hybrid Rocket Geometry" (B. Cantwell)
● Matt Subramanyam “A Universal Profile for Near-Wall Flows” (B. Cantwell)
● Elliot Ransom “Stretchable Sensor Network Smart Skins for IoT Applications" (F. Chang)
● Anthony Bombik "Multifunctional Energy Storage Composites" (F. Chang)
● Ben Estacio "Understanding Electromagnetic Pulse Generation from Hypervelocity Impacts on Spacecraft" (S. Close)
● Trevor Hedges "High-Resolution Plasma Physics Simulation of an Ablating Meteoroid" (S. Close)
● Nathan Stacey "Autonomous Orbit Determination and Asteroid Characterization" (S. D’Amico)
● Matthew Willis "Learning to Fly in Space: Virtual Reality Training for Spacecraft Formation Flight" (S. D’Amico)
● Gabriele Boncoraglio “Breaking the CPU Barrier Between High-Fidelity MDAO and Practical Design” (C. Farhat)
● Jonathan Ho "Towards Seamless MDAO of Aerospace Systems" (C. Farhat)
● Ashwin Kanhere “Quantifying Uncertainty of LiDAR Pose Estimates” (G. Gao)
● Adyasha Mohanty "A Particle Filtering Framework for Integrity Risk of GNSS-Camera Sensor Fusion" (G. Gao)
● Andy Castillo "Kinetic Modelling of Plasma and Gases" (K. Hara)
● Adnan Mansour "Hall Effect Thruster Modelling" (K. Hara)
● Anthony Corso "Validation of Safety-Critical Autonomous Systems Using Machine Learning" (M. Kochenderfer)
● Duncan Eddy "Scheduling At Scale: Automating Operations For Large Satellite Constellations" (M. Kochenderfer)
● Jean de Becdelievre “Improving Bilevel Design Optimization with Flexible Surrogate Modeling” (I. Kroo)
● Loren Newton “Online Training of Neural Networks for Nonlinear Aircraft System Identification.” (I. Kroo)
● Kristen Matsuno “Internal Regulation in Compressible Turbulent Shear Layers” (S. Lele)
● Gary Wu "Why does a supersonic jet sing in harmonic tones?" (S. Lele)
● Karen Leung “How to Expect the Unexpected: Ensuring Safety in Human-robot Interactions” (M. Pavone)
● Apoorva Sharma "Safe Learning-Enabled Autonomy" (M. Pavone)
● Adam Wiktor "Cooperative Multi-Robot Localization in Natural Terrain" (S. Rock)
● Aditya Mahajan "Autonomous Exploration of Complex Environments using Active SLAM" (S. Rock)
● Adam Caccavale "MSL-RAPTOR: A 6DoF Relative Pose Tracker for Onboard Robotic Perception" (M. Schwager)
● Kunal Shah "Aerial surveys with teams of drones in extreme environments: A case study on Antarctic penguin colonies" (M. Schwager)
● Ruiqi Chen "Mechanical Response of 3D Printed Structures" (D. Senesky)
● Savannah Eisner “Extended Exposure of Gallium Nitride Heterostructure Devices to a Simulated Venus Environment” (D. Senesky)
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Research Affiliate Posters
2020 Slides
Veronika Korneyeva "Experimental Studies of Flame Propagation in Opposed Flow: Planar Hybrid Fuel Samples and in Hybrid Rocket Geometry" (B. Cantwell)
● Matt Subramanyam “A Universal Profile for Near-Wall Flows” (B. Cantwell)
● Elliot Ransom “Stretchable Sensor Network Smart Skins for IoT Applications" (F. Chang)
● Anthony Bombik "Multifunctional Energy Storage Composites" (F. Chang)
● Ben Estacio "Understanding Electromagnetic Pulse Generation from Hypervelocity Impacts on Spacecraft" (S. Close)
● Trevor Hedges "High-Resolution Plasma Physics Simulation of an Ablating Meteoroid" (S. Close)
● Nathan Stacey "Autonomous Orbit Determination and Asteroid Characterization" (S. D’Amico)
● Matthew Willis "Learning to Fly in Space: Virtual Reality Training for Spacecraft Formation Flight" (S. D’Amico)
● Gabriele Boncoraglio “Breaking the CPU Barrier Between High-Fidelity MDAO and Practical Design” (C. Farhat)
● Jonathan Ho "Towards Seamless MDAO of Aerospace Systems" (C. Farhat)
● Ashwin Kanhere “Quantifying Uncertainty of LiDAR Pose Estimates” (G. Gao)
● Adyasha Mohanty "A Particle Filtering Framework for Integrity Risk of GNSS-Camera Sensor Fusion" (G. Gao)
● Andy Castillo "Kinetic Modelling of Plasma and Gases" (K. Hara)
● Adnan Mansour "Hall Effect Thruster Modelling" (K. Hara)
● Anthony Corso "Validation of Safety-Critical Autonomous Systems Using Machine Learning" (M. Kochenderfer)
● Duncan Eddy "Scheduling At Scale: Automating Operations For Large Satellite Constellations" (M. Kochenderfer)
● Jean de Becdelievre “Improving Bilevel Design Optimization with Flexible Surrogate Modeling” (I. Kroo)
● Loren Newton “Online Training of Neural Networks for Nonlinear Aircraft System Identification.” (I. Kroo)
● Kristen Matsuno “Internal Regulation in Compressible Turbulent Shear Layers” (S. Lele)
● Gary Wu "Why does a supersonic jet sing in harmonic tones?" (S. Lele)
● Karen Leung “How to Expect the Unexpected: Ensuring Safety in Human-robot Interactions” (M. Pavone)
● Apoorva Sharma "Safe Learning-Enabled Autonomy" (M. Pavone)
● Adam Wiktor "Cooperative Multi-Robot Localization in Natural Terrain" (S. Rock)
● Aditya Mahajan "Autonomous Exploration of Complex Environments using Active SLAM" (S. Rock)
● Adam Caccavale "MSL-RAPTOR: A 6DoF Relative Pose Tracker for Onboard Robotic Perception" (M. Schwager)
● Kunal Shah "Aerial surveys with teams of drones in extreme environments: A case study on Antarctic penguin colonies" (M. Schwager)
● Ruiqi Chen "Mechanical Response of 3D Printed Structures" (D. Senesky)
● Savannah Eisner “Extended Exposure of Gallium Nitride Heterostructure Devices to a Simulated Venus Environment” (D. Senesky)
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Students
Posters
Alejandro Campos
Algebraic Structure-Based Modeling of Turbulent Separated Flows
Ved Chirayath, Ashish Gael, Brian Mahlstedt
HiMARC3D
Joseph Kenahan, Eli Bashevkin, Brian Manning, Brian Mahlstedt
CubeSat Hemispherical Anti-Twist Tracking System (CubeMATTS)
Siddharth Krishnamoorthy
Alleviation of Reentry Blackout through Plasma Instabilities
Nicolas Lee, Ashish Goel, Theresa Johnson
Hypervelocity Impact Experiment
Alan Li
Existing Debris
Ashley Micks
Catalysis of N20 Decomposition: Harvesting Energy from Nitrogen Waste
Brandon Morgan
Large-Eddy and RANS Simulations of a Normal Shock Train in a Constant-Area Isolator
David Murakami
Active Cooling and Thermal Propulsion for Aerogravity Assist Vehicles
Pavan Narsai
Nozzle Erosion in Long Burn Duration Rockets
Benjamin Waxman
Peregrine Sounding Rocket
David Williams, Patrice Castonguay, Manuel López
High-Order Energy-Stable Schemes for Navier-Stokes Equations
Jonathan Yee
Diffusion Properties of Plasma in Nonspecular Trails
Jonathan Zimmerman
Self-Pressurizing Propellant Tank Dynamics
Andreas Zoellner
The Drag-Free CubeSat
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Students
Posters
David Amsallem, Kevin Carlberg, Julien Cortial, Matthew Zahr, Kyle Washabaugh, Todd Chapman, Sunil Deolalikar
Fast Simulations for Time-Critical Applications Using Model Order Reduction
Caitlin Chapin
Gallium Nitride Physical Strain Sensors for Extreme Harsh environments
Aniket Inamdar
Contrail LES: Sensitivity to Ice Microphysics and Ambient Parameters
Joseph Kocheemoolayil
Airfoil Trailing Edge http://aa-dev.stanford.edu/node/add/stanford-pageNoise
Siddharth Krishnamoorthy
Reentry Blackout Alleviation Through Electron Bunching
Alan Li
Space Environment Modeling, Prediction, and Mitigation of Orbital Debris
José Padial, Shandor Dektor
Correlating Sidecan Sonar with Bathymetry for AUV Navigation
Colleen Rosania
Smart Manufacturing of Multifunctional Materials
Timothy Szwarc
Thermal Mapping and Trends of Mars Analog Materials in Sample Acquisition Operations
Paul Tarantino
Modeling Hypervelocity Impact RF Emission
Brendan Tracey
Enhancing Turbulence Models Using Machine Learning
Rick Zhang, Nicholas Moehle, Christopher Pepper
Modeling and Experimental Analysis of Mobility-on-Demand Systems
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Andrew Bylard, Benjamin Hockman
Robust Capture and Deorbit of Rocket Body Debris Using Controllable Dry Adhesion
Autonomous Surface Mobility on Small Solar System Bodies
Ruiqi Chen, Ruth Miller
3D-Printed Radiation Shielding Nanocomposites
UV Sensors for CubeSats and Shock Tubes
Aniket Inamdar, Akshay Subharamaniam, Man Long Wong
Parametric Dependence of Contrails – Towards a Reduced Order Model
A Unified High Order Eulerian Framework for Large Deformation Elastic-Plastic “Flow” in Solids Coupled to Fluids
High-Order Adaptive Mesh Refinement Framework for Shock-Induced Mixing
A Computational Framework for Supersonic Parachute Inflation Dynamics
Order Reduction of Highly Nonlinear Structural Models with Contact
Active Vision-based Perception for Fast 3D Reconstruction with an Aerial Robot
Active Trajectory Classification for Motion-based Communication of Robots
Policy Compression for Aircraft Collision Avoidance Systems
Optimal Aircraft Rerouting during Commercial Space Launches
Multifunctional Energy-Storage Composites
State Sensing & Awareness for Fly-by-fell Intelligent Aerial Vehicles
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The Department of Aeronautics & Astronautics Faculty Openings
The 24-25 faculty opening closed Friday, January 17, 2025.
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Aero/Astro Celebration of Graduates
Aero/Astro Celebration of Graduates Video
Stanford Engineering Class of 2020
University Celebration Livestream
Aero/Astro Commencement Awards 2019-2020
Ballhaus Prize for best PhD Thesis
Sumeet Singh
Dr. William Ballhaus was educated at Stanford University and the California Institute of Technology. His distinguished career in industry included the position of Chief Designer and Vice President of Northrop Grumman, and President of Beckman Instruments. The prize named after him is presented annually to recognize the graduate student who created what is judged by a Faculty committee to be the best PhD dissertation for that year.
Nicholas J. Hoff Award for Outstanding Master's Degree Student
Allan Shtofenmakher
This award is funded by the Nicholas J. Hoff Scholarship, generously supported by Bernard Ross MS '59 Eng, PHD '65 Eng.Professor Nicholas J. Hoff received his PhD degree from Stanford in 1942. In 1957, he was asked by Stanford Provost Frederick Terman to return to Stanford and start an independent department of Aeronautics & Astronautics. The Award named after him is presented annually to the AA student identified as the top student based on the excellence and rigor of the student's Master's program.
Robert H. Cannon, Jr. Summer Doctoral Fellowship
Rachael Elizabeth Tompa
Although he joined the Department in 1959, Professor Robert Cannon served from 1966 until 1968 as Chief Scientist of the US Air Force at the Pentagon, from 1970 until 1974 as Assistant Secretary of Transportation for Research and Development, and from 1975 until 1979 as the Dean of the School of Engineering at the California Institute of Technology. In 1979, he returned to Stanford and taught in the Department until he retired in 1995. The award named after him was created by the Department of Aeronautics and Astronautics in recognition of his service as Chairman from 1979 to 1990. Its sustainability was made possible thanks to the Chiang Family Endowment. The award was created to assist students in their pursuit of the highest standards of academic leadership, cooperation, and research.
Sharon Kay Stanaway Award Summer Doctoral Fellowship
Christine Mary Hamilton
Dr. Sharon Kay Stanaway was a PhD student in the Department of Aeronautics and Astronautics from 1982 to 1988 and the beloved wife of Professor Ilan Kroo. Established in her memory, this award consists of a scholarship given to support the summer research activities of a woman engineering student in Aeronautics and Astronautics, reflecting Sharon's belief in the importance of graduate education and women's role in engineering. It is presented annually to an exceptional graduate student who shares Dr. Stanaway's interest and enthusiasm for aerospace.
Stanford AIAA Award Excellence in Teaching
Professor Juan Alonso
This award is presented annually by Stanford's chapter of the American Institute of Aeronautics and Astronautics to recognize a faculty member for their outstanding contributions in teaching.
Stanford AIAA Award Outstanding Course Assistant
Matt Subrahmanyam
This award is presented annually by Stanford's chapter of the American Institute of Aeronautics and Astronautics to recognize a student for their outstanding contributions in teaching.
2020 Centennial TA Award
Matt Subrahmanyam
This award is presented annually by the School of Engineering to recognize and reward a Teaching Assistant for outstanding contributions in teaching. The candidates are nominated by all Departments in the School of Engineering.
Outstanding Staff Award
Jenna Chay and Patrick Ferguson
This award is presented annually by the Department of Aeronautics and Astronautics to recognize a staff member for exceptional contributions to the Department.
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2024 FUTURE LEADERS IN AEROSPACE SYMPOSIUM
Sponsored by Stanford & MIT Departments of Aeronautics and Astronautics; the Ann & H.J. Smead Department of Aerospace Engineering Sciences at the University of Colorado Boulder, and the Penn State Department of Aerospace Engineering.
APPLY HERE
ABOUT
The Future Leaders in Aerospace Symposium aims to help the professional development of Aerospace Engineering professionals in academia and research. It will be hosted by the Stanford Department of Aeronautics and Astronautics (AeroAstro) in May 16-17 2024. The two day symposium will include a keynote address, round table discussions, and faculty panels, which will include topics such as:
- The job search/interview process
- Paths to academia and industry
- Tenure and promotion
- Work-life balance
Participants will be able to present their research to the attendees during “job talk starts,” gain career skills, engage with mentors, discuss emerging trends in aerospace engineering, and connect with a cohort of their peers.
Participants will be selected through a competitive application process. Applicants must be within 1-2 years of receiving their doctoral degree at the time of the workshop or must have received their PhD no earlier than 2021, and do not currently hold a faculty position. We expect that approximately 40 participants will be selected based on their academic excellence, interest in a research career in aerospace or related disciplines.
Contact: Jenny Scholes (jscholes@stanford.edu)
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Student Groups
Student Advisory Committee (SAC)
The SAC works on projects geared toward navigating your academic experience in the Aero/Astro Department, and serves as the liaison between faculty and students. Recent accomplishments include updates to the PhD coursework requirement, facilitating the Annual Student Survey, creating a CA Reference Guide, and maintaining a fellowship resources page on our website. The SAC meets once per month (~2 hours) during the academic year. Members are appointed to one or two of the current projects, dependent upon the time commitment of the specific undertaking. Each member is expected to serve for the full academic year (hopefully longer). The overall time commitment, including meetings, is about 4-5 hours per month. We also meet once per quarter with our Department Chair Professor Farhat about the state of the department, upcoming initiatives, and resolving student concerns. Please contact Patrick Ferguson at: patrickf@stanford.edu if you are interested in attending a meeting.
Comments? Questions? Suggestions? We'd love to hear from you!
Feedback form (SUNetID required, but is anonymous)
American Institute of Aeronautics and Astronautics (AIAA)
The Stanford student branch of the American Institute of Aeronautics and Astronautics (AIAA) holds various activities throughout the school year. Stanford’s AIAA activities are open to all students in the department (and those in other departments who are interested in Aero/Astro). Membership in AIAA is optional, although student memberships are available and include many non-Stanford benefits. For announcements of events, there is an AIAA email distribution list, aiaa@lists.stanford.edu.
President: Anshuk Chigullapalli
Vice President, Professional: Guillem C Vila
Vice President, Social: Michael Kwara
Treasurer: Kendall Seefried
Officer: Lauren Simitz
Officer: Aliza Fisher
Officer: Yuji Takubo
Women in Aeronautics & Astronautics (WIAA)
WIAA is part of Stanford's Student Chapter of AIAA (American Institute of Aeronautics and Astronautics). WIAA aspires to both support female and nonbinary students in Aero Astro as well as foster broader community within the discipline through professional, educational, social, and outreach events. All are welcome.
President: Lauren Simitz
Vice President: Efaine Chang
Treasurer: Somrita Banerjee
Current Events & Programs
- WIAA Mentorship Program
- We pair new and returning AA students to have conversations about grad school, Stanford, and/or just life more generally.
- If that wasn't incentive enough, we pay for you both to grab coffee/beverages once a quarter.
Membership in WIAA is optional, although student memberships are available and include many non-Stanford benefits. For announcements of events, there is an WIAA email distribution list, wiaa-members@lists.stanford.edu . We have an Instagram (wiaa_stanford) if you want to follow what we are up to! We love to showcase the events we run and feature our members.
Young Astronauts Program
The Young Astronauts program supports the local elementary schools. It relies on enthusiastic graduate student volunteers to interest young students in science and engineering. To subscribe to the email distribution list, young-astronauts@lists.stanford.edu, go to https://mailman.stanford.edu/mailman/listinfo/young-astronauts.
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Vance D. and Arlene C. Coffman Professor and the James and Anna Marie Spilker Chair of the Department of Aeronautics and Astronautics
Juan Alonso
Vance D. and Arlene C. Coffman Professor and the James and Anna Marie Spilker Chair of the Department of Aeronautics and Astronautics
Prof. Alonso is the founder and director of the Aerospace Design Laboratory (ADL) where he specializes in the development of high-fidelity computational design methodologies to enable the creation of realizable and efficient aerospace systems. Prof. Alonso’s research involves a large number of different manned and unmanned applications including transonic, supersonic, and hypersonic aircraft, helicopters, turbomachinery, and launch and re-entry vehicles. He is the author of over 200 technical publications on the topics of computational aircraft and spacecraft design, multi-disciplinary optimization, fundamental numerical methods, and high-performance parallel computing. Prof. Alonso is keenly interested in the development of an advanced curriculum for the training of future engineers and scientists and has participated actively in course-development activities in both the Aeronautics & Astronautics Department (particularly in the development of coursework for aircraft design, sustainable aviation, and UAS design and operation) and for the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. He was a member of the team that currently holds the world speed record for human powered vehicles over water. A student team led by Prof. Alonso also holds the altitude record for an unmanned electric vehicle under 5 lbs of mass.
Education
PhD, Princeton University, Mechanical & Aerospace Engineering (1997)
M.A., Princeton University, Mechanical & Aerospace Engineering (1993)
B.S., Massachusetts Institute of Technology, Aeronautics & Astronautics (1991)
Contact
Mail Code
4035
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Cyber Safety for Transportation
We recognize that transportation is changing dramatically.
Soon, cars will drive while the enclosed humans snooze or send text messages. Trains will slow and speed, certain that they alone occupy the underlying track. Airborne drones will fly confidently between buildings to monitor air pollution and order in our cities. These innovations will make transportation safer and impose much less harm on our environment. However, this new world of movement will need to be protected from cyber hackers. These actors will ply their trades on the mobile transactions between these driverless vehicles rather than rack-bound computers. Together with Computer Science, Electrical Engineering and Physics, we are pursuing a research effort aimed at blunting this danger to the future of transportation.
Today, pilots bound for Juneau, Alaska, follow the twisting Gastineau Channel above.
The capital city of Alaska lies at the end of this channel. On a clear day, our pilots enjoy the beautiful mountains in the Alaskan panhandle. On a dark and stormy night, they rely on failsafe satellite navigation to guide their aircraft down this forbidding fjord. Specifically, they rely on the navigation system to warn them within a few seconds if the location error may be larger than one hundred meters. They count on the residual risk to be lower than one approach per 10 million.
Tomorrow, automatic vehicles will descend from the air and populate our roadways (automatic driving assistance systems or ADAS), railways (positive train control, PTC) and waterways (ships without crews). Moreover, the sky will be filled with aircraft that carry no pilots to mitigate flight risk. These will be drones or unpiloted air vehicles (UAVs). For efficiency, cars will drive while the enclosed humans snooze or send text messages. Trains will slow and speed, certain that they alone occupy the underlying track. Drones will fly confidently between buildings to monitor air pollution and order in our major cites. All told, the benefits to safety and the environment will be great.
Future challenge
However, cyber hackers will now ply their trades on the mobile transactions between these new driverless vehicles rather than rack-bound computers. They will use jammers to deny vehicle guidance at the worst of times. They will use spoofers to misdirect the driving machines and eavesdropping to steal sensitive information. Navigation jammers are simple and already commonplace: They use strong radio signals to overwhelm (jam) the radio signals used for navigation and surveillance. Such attacks are not artful; they do not introduce misdirection, but simply deny the guidance service. In contrast, spoofers are complicated and dangerous. They replace the authentic navigation signals with counterfeit signals that misdirect the navigation system without detection.
Our goal
Cyber safety for transportation will not be provided with one stroke of the pen or keyboard. It will require legal elements to discourage jamming and spoofing. It will also require social protocols that broadcast the inappropriateness of such dangerous activities. Most importantly, it will require technical work to toughen the navigation receivers with new satellite signals, digital message authentication, intelligent antennas and inertial sensors. Safety against jammers and spoofers will also require us to augment current navigation with completely independent sources of time and location; perhaps these diverse sources will be placed in low-earth orbit or use terrestrial transmitters.
To this effect, our research effort for achieving cyber safety for transportation, led by the GPS Laboratory and Navigation and Autonomous Systems Laboratory (NAV Lab), is multidisciplinary: It combines Aeronautics, Astronautics, Computer Science, Law, Biology, Electrical Engineering and Physics. Simply put, our research effort in this area combines these disciplines to blunt the cyber danger to the future of transportation, and to ensure that the sought-after gains in safety and efficiency are achieved.
The Center for Automotive Research at Stanford (CARS) brings together researchers, students, industry, government and the community to enable a future of human-centered mobility. Understanding how people and machines work together has never been so important than when building vehicles of the future. CARS supports educational experiences for students, infrastructure for research and events that bring students and campus researchers together with industry professionals and the broader Stanford mobility community.
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Aerospace Robotics Laboratory
The ARL creates experimental systems for developing advanced robot systems and new control techniques with applications to free-flying space robots, undersea and air systems, mobile ground robots and industrial automation.
The focus is on the human-robot team, with the human at the strategy and task-command level and the robot system doing the real-time planning and precise task execution. The modus operandi is to pursue entirely new control system concepts, one after another, to full experimental proof of concept. Outdoor and indoor precision GPS (2 cm) systems are an integral part of each of the above vehicle systems (except undersea). Joint projects are underway with the Computer Science Robotics Laboratory in the full vertical integration of task conceptualization, planning and quick, precise execution. Experimental extension of these concepts to deep-underwater robotic vehicle development is being advanced with the Monterey Bay Aquarium Research Institute.
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Multifunctional Materials and Intelligent Structures
Several of our faculty are working at the forefront of materials and structures development.
- The Structures and Composites Laboratory (SACL) has led the design of integrated structures, development of smart structures, quantification of the damage tolerance of composite structures and development of advanced multifunctional materials. It has played a key role in the realization of new materials and structures in actual aerospace and transportation systems.
- The EXtreme Environment Microsystems Laboratory (XLab) is developing MEMS sensors, nanoelectronics and nanostructured materials that can withstand high temperatures, radiation exposure and chemical attack. It leverages the Stanford Nanofabrication Facility and the Stanford Nano Shared Facilities to create and examine nanomaterials, MEMS sensors and radiation-hardened, temperature-tolerant electronics. It has played a key role in the development of next-generation sensors and electronics for space exploration, combustion, satellites and subsurface well bores.
- The Reconfigurable & Active Structures Lab investigates structures that do more than carry loads. They study the connection between a structure’s performance and form allowing us to change geometry, mechanics, and multi-physics response (e.g. electromagnetic, self-sensing, optical) of a structure. The ultimate goal is the realization of reconfigurable spacecraft structures and scientific instruments with on-demand performance, helping reduce weight and energy use.
- The Morphing Space Structures Lab develops deployable spacecraft structures, novel flexible composite structures, and origami-inspired structures.
Electronics for extreme environments
Together, our SACL and XLab laboratories are examining the high-strength and lightweight properties of nanomaterials and nanoelectronics to advance aerospace systems and subsystems. They are also embedding robust piezoelectric sensors in composites to monitor the structural health of aircraft systems. At the fundamental level, their research focuses on the following areas in advanced materials and structures:
- Smart structures
- Multifunctional materials
- Structural health monitoring
- Damage tolerance of composite materials
- High-temperature piezoelectric transducers
- Nanocomposite materials
- MEMS sensors and electronics
- Nanoscale sensors for harsh environments
- Nanoceramic materials for harsh environments
- Radiation-hardened electronics
Intelligent and nanoscale materials
Our success in these areas leverages our core competencies in design manufacturing and experimental characterization of advanced structures and materials. Ultimately, the maturity and scalability of nanomaterials and the advancement of structural health monitoring approaches will change the way we engineer aircraft, automobiles, spacecraft, satellites and planetary rovers.
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News & Press Coverage
Tucked into the rear corner of Stanford University’s Cantor Arts Center, Livien Yin’s Thirsty might be easy to miss. Made up of just thirteen works, the exhibition presents the Brooklyn-based artist’s ongoing series “Paper Suns,”…
In the introduction to the catalogue for Spirit House, an exhibition at Stanford’s Cantor Arts Center through Sun/26, curator Aleesa Pitchamarn Alexander writes about how she, like her mother and grandmother, had sleep paralysis,…
Lek is featured in “Spirit House” at the Cantor Arts Center at Stanford University. The exhibition considers how 33 contemporary artists of Asian descent challenge the boundary between life and death through art, including how the…
How do we contend with loss that transcends the individual—in other words, loss that becomes collective, loss untethered from linear time or geographical boundaries? This complex set of questions emerged throughout “Spirit House,” a group…
How do you define a ghost? Typically we think of ghosts as the spirit of someone who passed away, who returns to haunt the living. What if a ghost is also a family secret that is passed down through generations? Or the memory of someone we only…
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Main content start
Past Events
Date
Thursday, April 10, 2025. 6:00pm - 7:30pm
Speaker:
Tiffany Sia
Please join us for the spring 2025 Distinguished Lecture in Asian Art in Honor of the Lijin Collection, featuring Tiffany Sia in a discussion with Stanford faculty members Pavle Levi and…
Date
Wednesday, January 22, 2025. 12:00pm - 1:00pm
Speaker:
Livien Yin
Join us for a casual conversation over boba with Livien Yin.
Date
Tuesday, January 21, 2025. 6:00pm - 7:30pm
Speaker:
Heesoo Kwon, Livien Yin
Join artists Heesoo Kwon and Livien Yin for an intimate conversation about their friendship and the continuous cycle of “contagious inspiration” that each offers the other.
Date
Thursday, January 16, 2025. 12:00pm - 1:00pm
Speaker:
Kathryn Cua
Join Kathryn Cua, Curatorial Assistant for the Asian American Art Initiative Asian American Art Initiative (AAAI) at the Cantor Arts Center…
Date
Wednesday, January 15, 2025. 12:00pm - 12:45pm
Speaker:
Aleesa Pitchamarn Alexander
Join us for a tour with Aleesa Pitchamarn Alexander, curator of the exhibition Spirit House, in which ancestral…
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Our Mission
The Asian American Art Initiative (AAAI) advances research, education, community engagement, and public access to the work of Asian American/diasporic artists and makers. Primarily based at the Cantor Arts Center, the AAAI strives to build one of the most significant museum collections of Asian American art and make it available to all through the museum’s curatorial program. We model an innovative art history that centers primary sources (works of art, archives, oral histories) to generate collaboration among artists, scholars, students, and community members; and across the museum, classroom, archive, and public.
About Us
The AAAI was co-founded in 2018 by Aleesa Pitchamarn Alexander and Marci Kwon. It is housed at the Cantor Arts Center, in partnership with the Department of Art & Art History, Stanford Libraries and Special Collections, and the Asian American Research Center at Stanford. Our actives include acquiring and conserving works of art and archives by Asian American/diasporic artists; activating them in research, education, and artist projects; and making them accessible through exhibition, digital projects, and public programming.
The AAAI approaches research, exhibitions, community engagement, and education as connected rather than separate endeavors. For example, the acquisition of a work of art might include discussions with Stanford Libraries and Special Collections to bring the artist’s papers or archives into their collections. Works and archival resources are presented to the public in exhibitions and permanent installations at the Cantor Arts Center, and become the basis for coursework in related undergraduate and graduate classes. These resources might also be activated in special research or artists projects, such as the online Martin Wong Catalogue Raisonné, performance collective For You’s audioguide tours for the exhibition East of the Pacific: Making Histories of Asian American Art, or publications such as “Asian American Art, Pasts and Futures,” in Panorama: Journal of the Association of Historians of American Art.
Our use of “Asian American” reflects the term’s Bay Area roots in interethnic, anti-colonial solidarity, while also acknowledging the distinct histories, experiences, and relationships to American imperialism contained by the category. Rather than imposing a homogenizing, fixed frame upon artists and works of art, we allow them to relate to terms denoting race, ethnicity, nationality, gender, and sexuality in whatever way they wish.
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Our Team
Aleesa Pitchamarn Alexander
Aleesa Pitchamarn Alexander oversees the collection of modern and contemporary art at the Cantor Arts Center. At the Cantor, she is the curator of Spirit House (2024), Livien Yin: Thirsty (2024), East of the Pacific: Making Histories of Asian American Art (2022), and The Faces of Ruth Asawa (2022). Her accompanying catalogue to Spirit House is the museum’s first major publication of the AAAI. Alexander leads the AAAI’s curatorial program and collection building, working with artists, artist estates, galleries, and collectors.
Marci Kwon
Marci Kwon is an award-winning art historian, writer, and teacher. Her work explores alterity, minorness, value, and the ethics of relation in art and material culture, with a special focus on the history of Asian American/diasporic artists and makers. She spearheads the AAAI’s research and education activities, and works closely with Stanford Libraries & Special Collections.
Kathryn Cua
Kathryn Cua is the Curatorial Assistant for the AAAI. Kat’s work is primarily focused at the Cantor where she provides administrative support to Aleesa Pitchamarn Alexander. Kat also provides research support for AAAI-related collections and exhibitions. She has assisted with Spirit House (2024) and Livien Yin: Thirsty (2024) and curated Archive Rooms: Bernice Bing (2024), a pilot presentation at the museum highlighting the art historical resources available at Stanford Libraries’ Special Collections.
Maggie Dethloff
Maggie Dethloff is the Assistant Curator of Photography and New Media at the Cantor Arts Center. Among her other activities, Maggie supports the AAAI by collaborating on acquisitions and exhibitions of Asian American artists working in photography, film, video, and digital media. At the Cantor, Maggie has curated the AAAI-related exhibitions TT Takemoto: Remembering in the Absence of Memory (2024), Kenneth Tam: All of M (2023), At Home/On Stage: Asian American Representation in Photography and Film (2022), and A young Yu: Mourning Rituals (2022).
Lindsay King
Lindsay King (she/her) is the Head Librarian of Bowes Art and Architecture Library, where she manages both services and collections, including circulating, digital, and rare materials related to art, architecture, and art history, for Stanford Libraries. Prior to coming to Stanford, she worked as an art librarian at Yale University and Northwestern University, and as a museum educator at the Art Institute of Chicago.
Anna Lee
Anna Lee collects photographic materials for research and teaching at Stanford. She also promotes collections and archives through writing, exhibitions, and object-based classes and often works collaboratively with colleagues in the AAAI. In 2023, she curated Gina Osterloh: Mirror Shadow Shape and, along with Ben Stone, Focus on Community: The Ricardo Alvarado Photography Archive at Stanford. In 2024, she stewarded the acquisition of photographer, curator, and community activist Irene Poon’s archive for Special Collections.
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Past Exhibitions
Spirit House
September 4, 2024–January 26, 2025
Throughout Southeast Asia, various belief systems and cultural practices make the consideration of life and death—and the permeability between these worlds—a daily exercise. In Thailand, a commonplace mode of engagement with the spiritual realm comes in the form of spirit houses, dollhouse-size devotional structures rooted in Buddhist and animist beliefs and found outside virtually every home or building.
Inspired by these structures, Spirit House surveys how thirty-three contemporary artists of Asian descent are exploring modes of making that exceed rational understanding and enter haunted dimensions. Challenging the privileging of data-driven, scientific methods of understanding the world around us, the artists represented in Spirit House instead foreground inherited, embodied, and psychic forms of knowledge.
Livien Yin: Thirsty
August 21, 2024 - February 22, 2025
Livien Yin: Thirsty is the first museum solo exhibition of the work of Brooklyn-based artist Livien Yin, a 2019 Stanford MFA. This single-gallery exhibition showcases new and recent paintings by Yin and their sensitive, researched-based approach to creating scenes of contemporary subjects alongside historical Asian Americans and their environments. In their paintings, Yin often casts their friends as models, collapsing the distance between the past and present to create new connective threads between Asian Americans across generations.
TT Takemoto: Remembering in the Absence of Memory
June 19 , 2024- December 1, 2024
This single gallery exhibition features two video works and two complementary series of small handmade objects and works on paper by San Francisco Bay Area-based artist TT Takemoto. Takemoto’s videos Looking for Jiro (2011) and On the Line (2018) uniquely center queer experiences of intimacy in prewar and WWII contexts. The Gentleman’s Gaman series (2009–23) and an installation of handcrafted kokeshi dolls (2023) offer sculptural, expanded modes of engagement with challenging and overlooked narratives in Asian American history, as reimagined by Takemoto.
Kenneth Tam: All of M
May 31–November 12, 2023
Taking its inherent theatricality as a jumping off point, Kenneth Tam’s video All of M re-stages the high school prom—often understood as a rite of passage in the transition to adulthood—as a vehicle for examining the affective spaces created by men in groups and how men perform their identities in spaces of social ritual.
A young Yu: Mourning Rituals
December 14, 2022–May 14, 2023
A young Yu’s work engages with Korean folklore, ritual, and dance, reinterpreting and regenerating it for contemporary, diasporic contexts. Mourning Rituals is a performance-based video reimagining the Korean ssitkimgut ritual, during which the spirits of the deceased are cleansed and guided into the afterlife.
At Home/On Stage: Asian American Representation in Photography and Film
August 31, 2022–January 15, 2023
One of three inaugural exhibitions of the Asian American Art Initiative (AAAI), At Home/On Stage: Asian American Representation in Photography and Film, curated by Maggie Dethloff, assistant curator of photography and new media, explores how Asian American artists’ work participates in conversations around identity and representation.
East of the Pacific: Making Histories of Asian American Art
September 28, 2022–February 12, 2023
For thousands of years, people have made treacherous journeys across bodies of water. Apart from Indigenous and First Nations peoples, all inhabitants of North America are the product of such transoceanic movement. This exhibition considers the ongoing artistic impact of many peoples’ migration across a particular body of water: the Pacific Ocean. What would it mean to understand the United States as being situated not just west of the Atlantic but east of the Pacific? How would this understanding reorient our perception of American art and its significant participants?
Ian Cheng: Emissary Sunsets The Self
June 30, 2021–November 13, 2022
Emissary Sunsets The Self explores cognitive evolution and the conditions that shape it. Created with a video game engine, the work generates open-ended animations without fixed outcomes—a format the artist calls “live simulation.” Ian Cheng—who studied cognitive science and art practice—utilizes a range of artificial intelligence (AI) models to mimic humans’ complex brains and enable unpredictable encounters between a character with a narrative goal and an erratic environment.
Stephanie Syjuco: “I AM AN…”
November 14, 2018–November 19, 2020
Stephanie Syjuco’s (Art Practice MFA ’05) monumental handmade banner, I Am An ..., is a powerful meditation on the connection between identity, protest, and political legibility in contemporary society. When completely expanded, the stark white block letters read I AM AN AMERICAN.
Do Ho Suh: The Spaces in Between
May 9, 2018–September 28, 2020
In this exhibition, artist Do Ho Suh uses a chandelier, wallpaper, and a decorative screen to focus attention on issues of migration and transnational identity. Using repetition, uniformity, and shifts in scale, Suh questions cultural and aesthetic differences between his native Korea and his adopted homes in the United States and Europe.
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Publications
2024
- Fostering noticing of classroom discussion features through analysis of contrasting cases. Blair, K.P., Banes, L.C. & Martin, L. (2024). Instructional Science 52, 417–452.
- Extended Realities and the Future of Knowledge Work: Opportunities and Challenges. Queiroz, A., Bailenson, J., Blair, K., Schwartz, D., Thille, C., & Wagner, A. (2024). Proceedings of the 31st IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW).
- Achieving an adaptive learner Daniel L. Schwartz (31 Oct 2024). Educational Psychologist.
2022
- Testing simple instructional models to promote the spontaneous transfer of inquiry strategies in new contexts. Saavedra, A. & Schwartz, D.L. (2022). Proceedings of the 16th International Conference of the Learning Sciences (ICLS).
- Assessment of Students' Feedback Behavior in A Game-Based Automated Feedback System-A Cross-Cultural Replication Study. Silvervarg, A., Blair, K., Cutumisu, M., & Gulz, A. (2022). In 30th International Conference on Computers in Education Conference, ICCE 2022 (pp. 292-301). Asia-Pacific Society for Computers in Education.
- Capturing students’ learning strategies in action using clickstream and eye-tracking data. Saavedra, A., Blair, K.P., Wolf, R., Marx, J.P., & Chin, D.B. (2022). In Proceedings of the 16th International Conference of the Learning Sciences (ICLS), pp. 1553-1557.
2021
- Physically active learning. Schwartz, D. L. (2021). SCIENCE, October 1, Vol 374, Issue 6563, p. 26.
- Finding formulas: Does active search facilitate appropriate generalization? Hallinen, N. R., Sprague, L. N., Blair, K. P., Adler, R. M., & Newcombe, N. S. (2021). Cognitive Research: Principles and Implications, 6(1), 1-18.
- Feedback choices and their relations to learning are age-invariant starting in middle school: A secondary data analysis. Cutumisu, M., & Schwartz, D. L. (2021). Computers & Education, 171, October, 104215.https://doi.org/10.1016/j.compedu.2021.104215
- Designs for learning with adaptive games and Teachable Agents. Kjällander, S. & Blair, K. P. (2021). In: Brooks, E. & Selander,S. Springer Nature: Emerging Practices and Technologies II. Design methodologies and digital learning ecologies..
2020
- C2STEM: A system for synergistic learning of physics and computational thinking. Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., Blair, K. P., Chin, D., Conlin, L., Basu, S., & McElhaney, K. (2020). Journal of Science Education and Technology, 29(1), 83-100.
- Modeling and analyzing inquiry strategies in open-ended learning environments. Käser T., Schwartz, D. L. (2020). International Journal of Artificial Intelligence in Education (IJAIED).
- The relation between academic achievement and the spontaneous use of design-thinking strategies. Cutumisu, M., Schwartz, D. L., & Lou, N. M. Computers & Education. Impact Factor: 5.63. Rank: 7/106 = 7%.
- How teachable agents influence students' responses to critical constructive feedback. Annika Silvervarg, Rachel Wolf, Kristen Pilner Blair, Magnus Haake & Agneta Gulz (2020). Journal of Research on Technology in Education, DOI: 10.1080/15391523.2020.1784812
2019
- Educating and measuring choice: A test of the transfer of design thinking in problem solving and learning. Journal of the Learning Sciences. Chin, D. B., Blair, K., Wolf, R., Conlin, L., Cutumisu, M., & Schwartz, D. L. (in press)
- Cognitive Science Foundations of Integer Understanding and Instruction. Sashank Varma, Kristen P. Blair & Daniel L. Schwartz. In A. Norton & M. Alibali (Eds.), Constructing number: Merging perspectives from psychology and mathematics education.
- A Digital Game-based Assessment of Middle-School and College Students' Choices to Seek Critical Feedback and to Revise. Cutumisu, M., Chin, D. B., & Schwartz, D. L. (2019). British Journal of Educational Technology, 50(6), 2977-3003. https://doi.org/10.1111/bjet.12796
2018
- The impact of critical feedback choice on students' revision, performance, learning, and memory. Computers in Human Behavior, 78, 351-367. Cutumisu, M., & Schwartz, D. L.
- Adaptive natural-language targeting for student feedback. Proceedings of the Fifth Annual ACM Conference on Learning at Scale, London, UK. Kolchinski, Y. A., Ruan, S., Schwartz, D., & Brunskill, E.
2017
- Assessing whether students seek constructive criticism: The design of an automated feedback system for a graphic design task. Maria Cutumisu, Kristen P. Blair, Doris B. Chin, & Daniel L. Schwartz. International Journal of Artificial Intelligence in Education (IJAIED), DOI: 10.1007/s40593-016-0137-5, Springer.
- Modeling exploration strategies to predict student performance within a learning environment and beyond. LAK, March 13-17, Vancouver, BC Canada. Käser, T., Hallinen, N. R, & Schwartz, D. L.
2016
- The ABCs of How We Learn: 26 Scientifically Proven Approaches, How They Work, and When to Use Them. Daniel L. Schwartz, Jessica M. Tsang, & Kristen P. Blair. W. W. Norton.
- A comparison of two methods of active learning in physics: Inventing a general solution versus compare and contrast. Doris B. Chin, Min Chi, & Daniel L. Schwartz. Instructional Science.
- Got game? A choice-based learning assessment of data literacy and visualization skills. Doris B. Chin, Kristen P. Blair, & Daniel L. Schwartz. Technology, Knowledge, and Learning.
- Commentary: The half-empty question for socio-cognitive interventions. Daniel L. Schwartz, Katherine M. Cheng, Shima Salehi, & Carl Wieman. Journal of Educational Psychology.
- Preparation for future learning: A missing competency in health professions education? M. Mylopoulos, R. Bydges, N.N. Woods, J. Manzone, & D.L. Schwartz. Medical Education.
- Can tinkering prepare students to learn physics concepts? Proceedings of the the 2016 Meeting of the American Society of Engineering Education, New Orleans, LA. Conlin, L. & Chin, D.B. (2016, June).
2015
- Posterlet: A game-based assessment of children's choices to seek feedback and to revise. Maria Cutumisu, Kristen P. Blair, Doris B. Chin, & Daniel L. Schwartz. Journal of Learning Analytics, Vol 2, Issue 1, 49-71.
- Learning to "See" Less Than Nothing: Putting Perceptual Skills to Work for Learning Numerical Structure. Jessica M. Tsang, Kristen P. Blair, Laura Bofferding, & Daniel L. Schwartz. Cognition and Instruction. DOI: 10.1080/07370008.2015.1038539
- Seeking the general explanation: A test of inductive activities for learning and transfer. Jonathan T. Shemwell, Catherine C. Chase, & Daniel L. Schwartz. Journal of Research in Science Teaching.
- Learning as coordination: Cognitive psychology and education. Daniel L. Schwartz, & Robert Goldstone. In L. Corno & E. M. Anderman (Eds.), Handbook of Educational Psychology, 3rd edition.
- Guardian angels of our better nature: Finding evidence of the benefits of design thinking. Proceedings of the 2015 Meeting of the American Society of Engineering Education, Seattle, WA. Selected for Best Papers session of the Design in Engineering Education Division. Conlin, L.D., Chin, D.B., Blair, K.P., Cutumisu, M., & Schwartz, D.L. (2015, June)
2014
- Give your ideas some legs: The positive effect of walking on creative thinking. Oppezzo, M., & Schwartz, D. L. Journal of Experimental Psychology: Learning, Memory, & Cognition. DOI: 10.1037/a0036577
- A pragmatic perspective on visual representation and creative thinking. Martin, L., & Schwartz., D. L. Visual Studies. DOI: 10.1080/1472586X.2014.862997
2013
- Experience and explanation: Using videogames to prepare students for formal instruction in statistics. Dylan Arena, & Daniel L. Schwartz. Journal of Science Education and Technology.
- The Bundling Hypothesis: How Perception and Culture Give Rise to Abstract Mathematical Concepts in Individuals. Kristen P. Blair, Jessica M. Tsang, & Daniel L. Schwartz. International Handbook of Research on Conceptual Change, 2nd Edition.
- Young Children Can Learn Scientific Reasoning with Teachable Agents.** Doris B. Chin, Ilsa M. Dohmen, & Daniel L. Schwartz. IEEE TLT special issue, Learning Systems for Science and Technology Education.
- Learning by Teaching Human Pupils and Teachable Agents: The Importance of Recursive Feedback. Sandra Y. Okita, & Daniel L. Schwartz. The Journal of the Learning Sciences.
- Measuring What Matters Most: Choice-Based Assessments for the Digital Age. Daniel L. Schwartz, & Dylan Arena. The MIT Press.
2012
- How to build educational neuroscience: Two approaches with concrete instances. Daniel L. Schwartz, Kristen P. Blair, & Jessica Tsang. British Journal of Educational Psychology Monograph Series II.
- Resisting overzealous transfer: Coordinating previously successful routines with needs for new learning. Daniel L. Schwartz, Catherine C. Chase, & John D. Bransford. Educational Psychologist.
- A value of concrete learning materials in adolescence. Kristen P. Blair, & Daniel L. Schwartz. In Reyna, V. F., Chapman, S., Dougherty, M., & Confrey, J. (Eds.), The adolescent brain: Learning, reasoning and decision making.
- Beyond natural numbers: Negative number representation in parietal cortex. Kristen P. Blair, Miriam Rosenberg-Lee, Jessica M. Tsang, Daniel L. Schwartz, & Vinod Menon. Frontiers in Human Neuroscience.
2011
- The mental representation of integers: An abstract-to-concrete shift in the understanding of mathematical concepts. Sashank Varma, & Daniel L. Schwartz. Cognition.
- Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Daniel L. Schwartz, Catherine C. Chase, Marily A. Oppezzo, & Doris B. Chin. In press. Journal of Education Psychology.
2010
- Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-Efficacy. Steven P. Dow, Alana Glassco, Jonathan Kass, Melissa Schwarz, Daniel L. Schwartz, & Scott R. Klemmer. ACM Transactions on Computer-Human Interaction.
- Preparing students for future learning with Teachable Agents. Doris B. Chin, Ilsa M. Dohmen, Britte H. Cheng, Marily A. Oppezzo, Catherine C. Chase, & Daniel L. Schwartz. In press. Educational Technology Research & Development.
2009
- Choice-based assessments for the digital age. Daniel L. Schwartz, & Dylan Arena. White paper for the MacArthur Foundation.
- Teachable agents and the protege effect: Increasing the effort towards learning. Catherine Chase, Doris B. Chin, Marily Oppezzo, & Daniel L. Schwartz. Journal of Science Education and Technology.
- Prospective adaptation in the use of external representations. Lee Martin, & Daniel L. Schwartz. Cognition & Instruction.
- Constructivism in an age of non-constructivist assessments. Daniel L. Schwartz, Robb Lindgren, & Sarah Lewis. In T. Duffy & S. Tobias (Eds.), Constructivist instruction: Success or failure.
- Interactive metacognition: Monitoring and regulating a teachable agent. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of Metacognition in Education.
2008
- Scientific and pragmatic challenges for bridging education and neuroscience. Sashank Varma, Bruce McCandliss, & Daniel L. Schwartz. Educational Researcher.
- How should educational neuroscience conceptualize the relation between cognition and brain function? Mathematical reasoning as a network process. Sashank Varma, & Daniel L. Schwartz. Educational Research.
- Dynamic Transfer and Innovation. Daniel L. Schwartz, Sashank Varma, & Lee Martin. In S. Vosniadou (Ed.), International Handbook of Research on Conceptual Change.
- Instrumentation and Innovation in Design Experiments: Taking the Turn to Efficiency. Daniel L. Schwartz, Jammie Chang, & Lee Martin. In A. Kelly & R. Lesh (Eds.), Design research methods in education.
2007
- Intercultural adaptive expertise: Explicit and implicit lessons from Dr. Hatano. Xiaodong Lin, Daniel L. Schwartz, & John D. Bransford. Human Development.
- It's a homerun! Using mathematical discourse to support the learning of statistics. Kathy Himmelberger, & Daniel L. Schwartz. Mathematics Teacher.
- The Mere Belief of Social Interaction Improves Learning. Sandra Okita, Jeremy Bailenson, & Daniel L. Schwartz. Cognitive Science Conference (2007).
- Animations of thought: Interactivity in the teachable agents paradigm. Schwartz, D. L., Pilner, K. B., Biswas, G., Leelawong, K., & Davis, J. In R. Lowe & W.Schnotz (Eds.), Learning with Animation: Research and Implications for Design. UK: Cambridge University Press.
- It is not television anymore: Designing digital video for learning and assessment. Daniel L. Schwartz, & K. Hartman. In R. Goldman, R. P Pea, B. Barron, & S. Derry (Eds.), Video research in the learning sciences.
- Pedagogical agents for learning by teaching: Teachable Agents. Kristen Blair, Daniel L. Schwartz, Gautam Biswas, & Krittaya Leelawong. Educational Technology.
- Reconsidering prior knowledge. Daniel L. Schwartz, David Sears, & Jammie Chang. In M. Lovett & P. Shah (Eds.), Thinking with Data. Mahwah, NJ: Erlbaum.
2006
- It takes expertise to make expertise: Some thoughts about why and how and Reflections on the Themes in Chapters 15-18. John D. Bransford, & Daniel L. Schwartz. To appear in A. Ericsson (Ed.), Handbook of Expertise.
- Designs for Knowledge Evolution: Towards a prescriptive theory for integrating first- and second-hand knowledge. Daniel L. Schwartz, Taylor Martin, & Na'ilah Nasir. In P. Gardenfors & P. Johansson (Eds.), Cognition, Education, and Communication Technology.
- Young Children's Understanding of Animacy and Entertainment Robots. Sandra Okita, & Daniel L. Schwartz. In International Journal of Humanoid Robotics, 3.
- Spatial representations and imagery in learning. Daniel L. Schwartz, & Julie Heiser. In K. Sawywe (Ed.), Handbook of the Learning Sciences (pp 283-298). Cambridge University Press.
- Distributed learning and mutual adaptation. Daniel L. Schwartz, & Taylor Martin. Pragmatics & Cognition,14, 313-332.
2005
- Efficiency and Innovation in Transfer. Daniel L. Schwartz, David Sears, & John D. Bransford. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 1 ‐ 51). CT: Information Age Publishing.
- How Mathematics Propels the Development of Physical Knowledge. Daniel L. Schwartz, Taylor Martin, & Jay Pfaffman. Journal of Cognition and Development.
- Physically Distributed Learning: Adapting and Reinterpreting Physical Environments in the Development of Fraction Concepts. Taylor Martin, & Daniel L. Schwartz. Cognitive Science.
- Learning by teaching: A new agent paradigm for educational software. Biswas, Schwartz, Leelawong, Vye, & TAG-V. Applied Artificial Intelligence, 19, 363-392.
- Towards teacher's adaptive metacognition. Xiaodong Lin, Daniel L. Schwartz, & Giyoo Hatano. Educational Psychologist, 40, 245-256.
2004
- Inventing to Prepare for Future Learning: The Hidden Efficiency of Encouraging Original Student Production in Statistics Instruction. Daniel L. Schwartz, & Taylor Martin. Cognition and Instruction.
Earlier Publications
- The construction and analogical transfer of symbolic visualizations. Daniel L. Schwartz. Journal of Research in Science Teaching, 30, 1309-1325.
- A time for telling. Daniel L. Schwartz, & John D. Bransford. Cognition & Instruction, 16, 475-522.
- The productive agency that drives collaborative learning. Daniel L. Schwartz. Collaborative learning: Cognitive and computational approaches, pp. 197-218.
- Rethinking transfer: A simple proposal with multiple implications. Daniel L. Schwartz, & John D. Bransford. Review of Research in Education, 24, 61-101.
- Software for managing complex learning: An example from an educational psychology course. Daniel L. Schwartz, Sean Brophy, Xiaodong Lin, & John D. Bransford. Educational Technology Research and Development, 47, 39- 59.
- The emergence of abstract representations in dyad problem solving. Daniel L. Schwartz. Journal of the Learning Sciences, 4, 321-354.
- Reflection at the crossroads of cultures. Xiaodong Lin, & Daniel L. Schwartz. Mind, Culture, & Activity.
- Tool use and the effect of action on the imagination. Daniel L. Schwartz, & Douglas L. Holton. Journal of Experimental Psychology: Learning, Cognition, and Memory.26, 1655-1665.
- Inferences through imagined actions: knowing by simulated doing. Daniel L. Schwartz, & Tamara Black. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 116-136.
- The role of mathematics in explaining the material world: Mental models for proportional reasoning. Daniel L. Schwartz, & Joyce L. Moore. Cognitive Science, 22, 471-516.
- Analog imagery in mental model reasoning: Depictive models. Daniel L. Schwartz, & John B. Black. Cognitive Psychology, 30, 154-219.
- Shuttling between depictive models and abstract rules: Induction and fallback. Daniel L. Schwartz, & John B. Black. Cognitive Science, 20, 457-497.
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Social Foundations of Learning
These projects are exploring how to make technologies that enhance the best of social interaction for learning while mitigating the worst.
Learning With Others
Learning with other people confers special benefits that can be difficult to achieve working alone. Our lab works to identify these benefits, so it is possible to answer the questions: (a) when is it ideal to use social learning arrangements, and (b) how can technologies facilitate or simulate productive social learning arrangements?
In one line of research, we have found that simply believing that one is interacting with another person leads to superior learning, compared to working alone or with a computer-controlled character. When people interact with other people they pay more attention, show moderate increases in arousal, and are more attentive to feedback.
In a separate line of work, we have tried to understand some of the unique outcomes of collaboration, especially in the context of "making things." We have found that collaborating yields more well-structured ideas. We have further theorized that producing ideas and seeing them taken up by others is the basis of human agency, and has exceptional benefits for learning.
Relevant publications:
- Okita, S. A., & Schwartz, D. L. (in press). Learning by teaching human pupils and teachable agents: A focus on recursive feedback. Journal of the Learning Sciences.
- Okita, S.Y., Bailenson, J., Schwartz, D. L. (2007). The mere belief of social interaction improves learning. In D. S. McNamara & J. G. Trafton (Eds.), The Proceedings of the 29th Meeting of the Cognitive Science Society (pp. 1355-1360). August, Nashville, USA.
- Schwartz, D. L. (1995) The emergence of abstract representations in dyad problem solving. Journal of the Learning Sciences, 4, 321-354.
- Schwartz, D. L. (1999). The productive agency that drives collaborative learning. In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 197-218). NY: Elsevier Science.
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Social Foundations of Learning
These projects are exploring how to make technologies that enhance the best of social interaction for learning while mitigating the worst.
R2
Values and social norms are the glue that makes learning through social interaction possible. Values are often invisible, which is one reason that tourists often misjudge the residents of a foreign country. Rate-and-Relate (R2) tries to facilitate safe conversations about the values that people hold dear, so people can come to understand one another and learn more effectively. It is primarily intended for teachers and their classrooms, but it can be used in many other settings. In the typical use, a teacher creates a prompt, for example, "What is the most important goal of this class?" The teacher provides five possible alternatives, for example: (a) to learn a few big ideas, (b) to get a good grade, (c) to explore one's interests, (d) to master all the content, (e) to be the very best student.
Working on-line individually, students and the teacher each sort the five alternatives from most important to least important (to them). The next step is the key twist that makes R2 effective for safe conversation. The students need to predict how the teacher ranked the alternatives, and the teacher needs to predict how the students ranked the alternatives.
When everyone has made their rankings, the teacher can project the results at the front of the class. The predictions that students have about their teachers and vice versa lead to safe and productive conversations about values - "Why did you think I would say that?" The goal of R2 is to help people come to understand one another and negotiate a common ground that enables social interaction and learning to go forward. This differs from telling the students they need to have the same values as the teacher, which simply does not work when students have very good reasons, often based on experiences and cultures foreign to the teacher, for their values.
Relevant publications:
- Schwartz, D. L. & Lin, X. D. (2003). Technologies for learning from intercultural reflections. Intercultural Education, 14, 291-306.
- Lin, X. D. & Schwartz, D. L. (2003). Reflection at the crossroads of cultures. Mind, Culture, & Activity, 10, 9-26.
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Social Foundations of Learning
These projects are exploring how to make technologies that enhance the best of social interaction for learning while mitigating the worst.
Teachable Agents
Teachable Agents (TA) is a learning technology that draws on the social metaphor of teaching a computer agent to help students learn. Students teach their agent by creating a concept map that serves as the agent’s “brain.” An artificial intelligence engine enables the agent to interactively answer questions posed to it by traversing the links & nodes in its map. As the agent reasons, it also animates the path it is following, thereby providing feedback, as well as a visible model of thinking for the students. Students can then use the feedback to revise their agent's knowledge (and consequently, their own).
The teaching metaphor enlists fruitful social attitudes during the interaction, including a sense of responsibility for one's agent that appears to motivate students to work harder to organize their understanding. TA has been found to improve children's scientific reasoning in both causal and taxonomic (hierarchical) domains.
Relevant publications:
- Okita, S. A., & Schwartz, D. L. (2013). Learning by teaching human pupils and teachable agents: The importance of recursive feedback. Journal of the Learning Sciences, 22(3), 375-412.
- Chin, D.B., Dohmen, I.M., & Schwartz, D.L. (2013). Teachable Agents make scientific thinking visible and improve learning for younger children. IEEE Transaction on Learning Technologies.
- Chin, D.B., Dohmen, I.M., Cheng, B.H., Oppezzo, M.A., Chase, C.C., & Schwartz, D.L. (2010). Preparing students for future learning with Teachable Agents. Educational Technology Research and Development, 58(6): 649-669. doi: 10.1007/s11423-010-9154-5
- Chase, C., Chin, D.B., Oppezzo, M, & Schwartz, D.L. (2009). Teachable agents and the protege effect: Increasing the effort towards learning. Journal of Science Education and Technology, 18(4), 334-352. doi: 10.1007/s10956-009-9180-4
- Blair, K., Schwartz, D. L., Biswas, G., & Leelawong, K. (2007).Pedagogical agents for learning by teaching: Teachable Agents. Educational Technology, 47(1), 56-61.
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STEM Builders
These projects are investigating the core foundations which build up conceptual reasoning in science, technology, engineering, and math (STEM) disciplines.
Pre-school Mathematics: Critter Corral
Children's early math skills are powerful predictors of later school achievement, and there has been an increased focus on math learning in preschool, particularly the development of a flexible understanding of number. This goes beyond simply knowing the counting sequence to include cardinality, relative magnitude, estimation, numeral identification, 1:1 correspondence, and set composition/ decomposition.
To help preschoolers develop a flexible understanding of number, we created Critter Corral, a freely available iPad app. The child's goal is to help return a Wild West town to its former glory by helping the town's businesses. In all games, the task is to create a 1:1 correspondence with a target amount. For example, to help the restaurant, learners count customers to tell chef how much food to cook. Count correctly and each customer happily gets one piece of food. Count too few, and the chef does not cook enough food and some customers are left hungry. The learner can fix it by adding or taking away food. We think this kind of feedback, which focuses learners on the quantitative discrepancies, will be particularly helpful for relative magnitude concepts (e.g., 3 is less than 5). In an ongoing study we compare it with other common feedback approaches for young children, including modeling the correct behavior, and allowing unlimited guesses until correct.
Conference presentation:
- Early Education and Technology for Children - April 2013, Salt Lake City, Utah.http://www.eetcconference.org
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Virtual Field Trips
Virtual field trips (VFTs) can take a variety of forms, but at their core, they enable learners to engage with a location, often without physically traveling there, in a digitally mediated way. VFTs can broaden access to locations and experiences and may help mitigate inequities due to cost, accessibility, or inclusivity. Advances in technology have reduced the barriers to creation, opening opportunities for educators and students to become content creators, embedding concepts in locally or personally meaningful contexts.
Critter Corral
To help preschoolers develop a flexible understanding of number, we created Critter Corral, a freely available iPad app. The child's goal is to help return a Wild West town to its former glory by helping the town's businesses. In all games, the task is to create a 1:1 correspondence with a target amount.
Teachable Agents
Teachable Agents (TA) is a learning technology that draws on the social metaphor of teaching a computer agent to help students learn. Students teach their agent by creating a concept map that serves as the agent's "brain." An artificial intelligence engine enables the agent to interactively answer questions posed to it by traversing the links & nodes in its map. As the agent reasons, it also animates the path it is following, thereby providing feedback, as well as a visible model of thinking for the students. Students can then use the feedback to revise their agent's knowledge (and consequently, their own).
Choice-Based Assessments: Idollet
In this game-based assessment, players combine different lights to make specific colors shine during the animals' musical performances. Players are required to think critically about resources in the game to help them understand additive color mixing. Using this assessment, we found that students' choice to engage in critical thinking to learn about the primary colors of light predicted 35% of the variance in their mathematics grades.
Stats Invaders
We designed a simple computer game called Stats Invaders. The game is modeled loosely after the arcade classic Space Invaders, but with the twist that the aliens are dropping from the sky according to a probability distribution. Your task is not simply to shoot the aliens, but also to determine which of two displayed distributions (normal or uniform). is generating the alien attack.
R2
Rate-and-Relate (R2) tries to facilitate safe conversations about the values that people hold dear, so people can come to understand one another and learn more effectively. It is primarily intended for teachers and their classrooms, but it can be used in many other settings. In the typical use, a teacher creates a prompt, for example, "What is the most important goal of this class?" The teacher provides five possible alternatives, for example: (a) to learn a few big ideas, (b) to get a good grade, (c) to explore one's interests, (d) to master all the content, (e) to be the very best student.
Working on-line individually, students and the teacher each sort the five alternatives from most important to least important (to them). The next step is the key twist that makes R2 effective for safe conversation. The students need to predict how the teacher ranked the alternatives, and the teacher needs to predict how the students ranked the alternatives.
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Majoring in the Program in African & African American Studies (AAAS)
The major consists of minimum of 60 units of approved courses.
All courses must be taken for a letter grade
Core Requirements (all courses must be taken for a letter grade)
Required Courses
5 units
Choose one of two required courses:
- Introduction to Themes in Black Studies I or II
AAAS Social Science Courses
5 units
Choose from:
- AFRICAAM4 - The Sociology of Music
- AFRICAAM41 - Genes and Identity
- AFRICAAM106 - Race, Ethnicity, and Linguistic Diversity in Classrooms: Sociocultural Theory and Practices
- AFRICAAM112 - Urban Education
- AFRICAAM130 - Community-based Research As Tool for Social Change:Discourses of Equity in Communities & Classrooms
- AFRICAAM146A - African Politics
- AFRICAAM165 - Identity and Academic Achievement
- AFRICAAM195 - Independent Study
- AFRICAAM245 - Understanding Racial and Ethnic Identity Development
- AFRICAST111 - Education for All? The Global and Local in Public Policy Making in Africa
- AFRICAST112 - AIDS, Literacy, and Land: Foreign Aid and Development in Africa
- AFRICAST135 - Designing Research-Based Interventions to Solve Global Health Problems
- AFRICAST142 - Challenging the Status Quo: Social Entrepreneurs Advancing Democracy, Development and Justice
- AFRICAST195 - Shifting Frames
- AFRICAST199 - Independent Study or Directed Reading
- AFRICAST211 - Education for All? The Global and Local in Public Policy Making in Africa
- AFRICAST212 - AIDS, Literacy, and Land: Foreign Aid and Development in Africa
- AFRICAST235 - Designing Research-Based Interventions to Solve Global Health Problems
- AFRICAST299 - Independent Study or Directed Reading
- AMSTUD121Z - Political Power in American Cities
- AMSTUD201 - History of Education in the United States
- ANTHRO27N - Ethnicity and Violence: Anthropological Perspectives
- ANTHRO32 - Theories in Race and Ethnicity: A Comparative Perspective
- ANTHRO138 - Medical Ethics in a Global World: Examining Race, Difference and Power in the Research Enterprise
- HUMBIO121E - Ethnicity and Medicine
- LINGUIST152 - Sociolinguistics and Pidgin Creole Studies
- LINGUIST156 - Language, Gender, & Sexuality
- LINGUIST252 - Sociolinguistics and Pidgin Creole Studies
- MUSIC147J - Studies in Music, Media, and Popular Culture: The Soul Tradition in African American Music
- POLISCI146A - African Politics
- SOC135 - Poverty, Inequality, and Social Policy in the United States
AAAS Humanities Courses
5 units
Choose from:
- AFRICAAM18A - Jazz History: Ragtime to Bebop, 1900-1940
- AFRICAAM18B - Jazz History: Bebop to Present, 1940-Present
- AFRICAAM19 - Studies in Music, Media, and Popular Culture: The Soul Tradition in African American Music
- AFRICAAM20A - Jazz Theory
- AFRICAAM30 - The Egyptians
- AFRICAAM31 - RealTalk: Intimate Discussions about the African Diaspora
- AFRICAAM43 - Introduction to English III: Introduction to African American Literature
- AFRICAAM45 - Dance Improvisation from Freestyle to Hip Hop
- AFRICAAM47 - History of South Africa
- AFRICAAM50B - Nineteenth Century America
- AFRICAAM105 - Intro to Black Studies/Intro to African American Studies II
- AFRICAAM133 - Literature and Society in Africa and the Caribbean
- AFRICAAM145B - Introduction to African Studies I: Africa in the 20th Century
- AFRICAAM147 - History of South Africa
- AFRICAAM150B - Nineteenth Century America
- AFRICAAM156 - Performing History: Race, Politics, and Staging the Plays of August Wilson
- AFRICAAM195 - Independent Study
- AFRICAAM199 - Honors Project
- AFRICAST199 - Independent Study or Directed Reading
- AFRICAST299 - Independent Study or Directed Reading
- AMSTUD51Q - Comparative Fictions of Ethnicity
- AMSTUD150B - Nineteenth Century America
- COMPLIT51Q - Comparative Fictions of Ethnicity
- COMPLIT149 - The Laboring of Diaspora & Border Literary Cultures
- DANCE30 - Contemporary Choreography: Chocolate Heads 'Garden After Dark' Performance Project
- DANCE45 - Dance Improvisation from Freestyle to Hip Hop
- DANCE58 - Hip Hop I: Introduction to Hip Hop
- DANCE108 - Hip Hop Choreography: Hip Hop Meets Broadway
- HISTORY50C - The United States in the Twentieth Century
- HISTORY106A - Global Human Geography: Asia and Africa
- HISTORY150C - The United States in the Twentieth Century
Coursework focused on the African continent via African Studies or AAAS
5 units
Choose at least one of the following:
- AFRICAAM30 - The Egyptians
- AFRICAAM47 - History of South Africa
- AFRICAAM48Q - South Africa: Contested Transitions
- AFRICAAM111 - AIDS, Literacy, and Land: Foreign Aid and Development in Africa
- AFRICAAM133 - Literature and Society in Africa and the Caribbean
- AFRICAAM145B - Introduction to African Studies I: Africa in the 20th Century
- AFRICAAM146A - African Politics
- AFRICAAM147 - History of South Africa
- AFRICAST112 - AIDS, Literacy, and Land: Foreign Aid and Development in Africa
- AFRICAST211 - Education for All? The Global and Local in Public Policy Making in Africa
- AFRICAST212 - AIDS, Literacy, and Land: Foreign Aid and Development in Africa
- HISTORY45B - Introduction to African Studies I: Africa in the 20th Century
- HISTORY47 - History of South Africa
- HISTORY48Q - South Africa: Contested Transitions
- HISTORY145B - Introduction to African Studies I: Africa in the 20th Century
- HISTORY147 - History of South Africa
- POLISCI146A - African Politics
Writing in the Major (WIM)
5 units
Complete at least 1 of the following Courses:
- AFRICAAM154G - Black Magic: Ethnicity, Race, and Identity in Performance Cultures
- AFRICAAM200X - Honors Thesis and Senior Thesis Seminar
Note: AFRICAAM200X also satisfies Capstone/Senior Seminar
Other AAAS courses
35 units
Earn at least 35 credits from the following:
- AFRICAAM18A - Jazz History: Ragtime to Bebop, 1900-1940
- AFRICAAM18B - Jazz History: Bebop to Present, 1940-Present
- AFRICAAM127 - Health Impact of Sexual Assault and Relationship Abuse across the Lifecourse
- AFRICAAM37 - Contemporary Choreography: Chocolate Heads 'Garden After Dark' Performance Project
- AFRICAAM41 - Genes and Identity
- AFRICAAM45 - Dance Improvisation from Freestyle to Hip Hop
- AFRICAAM52N - Mixed-Race Politics and Culture
- AFRICAAM106 - Race, Ethnicity, and Linguistic Diversity in Classrooms: Sociocultural Theory and Practices
- AFRICAAM111 - AIDS, Literacy, and Land: Foreign Aid and Development in Africa
- AFRICAAM112 - Urban Education
- AFRICAAM130 - Community-based Research As Tool for Social Change:Discourses of Equity in Communities & Classrooms
- AFRICAAM132 - Social Class, Race, Ethnicity, and Health
- AFRICAAM133 - Literature and Society in Africa and the Caribbean
- AFRICAAM145B - Introduction to African Studies I: Africa in the 20th Century
- AFRICAAM146A - African Politics
- AFRICAAM150B - Nineteenth Century America
- AFRICAAM157P - Solidarity and Racial Justice
- AFRICAAM159 - James Baldwin & Twentieth Century Literature
- AFRICAAM165 - Identity and Academic Achievement
- AFRICAAM192 - History of Sexual Violence in America
- AFRICAAM194 - Topics in Writing & Rhetoric: Contemporary Black Rhetorics: Black Twitter and Black Digital Cultures
Capstone Experience
- AFRICAAM200X - Honors Thesis and Senior Thesis Seminar
Note: AFRICAAM200X also satisfies WIM
Optional Thematic Emphases
Thematic emphases are optional course guides to help students focus their studies. They do not require specific coursework, but rather guide the topical focus for each student through their undergraduate career.
After deciding upon a thematic emphasis, students are advised to make an appointment with the faculty liaison for their respective emphasis to develop a relationship, get advice on coursework, seek out future research opportunities, and potentially advise or direct them to other advisors for honors/capstone research projects.
Politics & the Law
Faculty Director(s): Lauren Davenport and Rick Banks
This emphasis exposes students to inquiry and major topics in disciplines like public policy, government, and international relations.
Sample Courses:
- AFRICAAM 58Q American Landscapes of Segregation
- AFRICAAM 241 Race, Justice, & Integration
- AMSTUD 108 Race and the Law: Historical and Contemporary Perspectives
- AMSTUD 106 Spectacular Trials: Sex, Race, and Violence in Modern American Culture
- CSRE 229 Racial Justice through Law
Historical Inquiry
Faculty Director(s): Allyson Hobbs, Clayborne Carson, Jim Campbell
This emphasis exposes students to historical and historiographical views of the black experience in US and transnational contexts.
Sample Courses:
- AFRICAAM 252C The Old South: Culture Society, and Slavery
- AFRICAAM 18C Sugar, Slavery, Race & Revolution: The Caribbean, 1450-1888
- AFRICAAM 68D Martin Luther King Jr.: The Inner Life & Global Vision
- AFRICAAM 45S The Cold War & the Shaping of Modern Africa
- AFRICAAM 51 Hamilton: An American Musical
Identity & Intersectionality
Faculty Director(s): Jennifer Brody, Allyson Hobbs, Stephen Shigematsu
This multi-disciplinary thematic emphasis exposes students to fields that attend to questions of identity and analysis drawn from gender and sexuality studies, critical ethnic studies, religious studies, etc.
Sample Courses:
- AFRICAAM 226 Mixed Race Politics and Culture
- AFRICAAM 258X Black Feminist Theater & Theory
- AFRICAAM 236 Constructing Race & Religion
- AFRICAAM 54N African American Women’s Lives
- FEMGEN 97 Bow Down: Queer Hip-Hop Pedagogy
- AFRICAAM 121N How to Make a Racist
Art & Cultural Expression
Faculty Director(s): Michele Elam, Vaughn Rasberry, Jonathan Calm
This thematic emphasis focuses on disciplines that engage literature, performance studies, art and visual culture, cultural theory, etc.
Sample Courses:
- AFRICAAM 265G African American Independent Film
- AFRICAAM 159 James Baldwin & 20th Century Literature
- AFRICAAM 156 Race, Politics, & the Staging of August Wilson
- AFRICAAM 160J Conjure Art 101: Performances of Ritual, Spirituality, & Decolonial Black Feminist Magic
- AFRICAAM 18B Jazz History
- AFRICAAM 352 The Novel in Africa
- AFRICAAM 133 Literature & Society in Africa & The Caribbean
- AFRICAAM 45 Freestyle Improv from Contemporary to Hip Hop & Beyond
Media, Science, & Technology
Faculty Director(s): Adam Banks
This thematic emphasis focuses on disciplines that engage journalism and communications, digital studies, environmental studies, biotechnology, and science, technology, and society, etc.
Sample Courses:
- AFRICAAM 41 Genes and Identity
- AFRICAAM 81 Media Representations of Africa
- AFRICAAM 200N Technologies, Social Justice, & Black Vernacular Cultures
- AFRICAAM 122F Histories of Race in Science & Medicine at Home and Abroad
- AFRICAAM 223 Literature & Human Experimentation
Education, Policy, & Reform
Faculty Director(s): Arnetha Ball, Bryan Brown
This thematic emphasis focuses on issues related to education and education policy, linguistics, psychology, sociology, anthropology, etc.
Sample Courses:
- AFRICAAM 112 Urban Education
- AFRICAAM 106 Race, Ethnicity, & Linguistic Diversity in Classrooms
- AFRICAAM 21 African American Vernacular English
- AFRICAAM 241 Race, Justice & Integration
Social Impact & Entrepreneurship
Faculty Director(s): TBD
This thematic emphasis focuses on practice and the study of justice ideologies, social movements, social entrepreneurship, and community-based research, etc.
Sample Courses:
- AFRICAAM 106B Community Based Research as a Tool for Social Change
- AFRICAAM 32 Social Class, Race, Ethnicity, & Health
- AFRICAAM 111 Foreign Aid & Development in Africa
- AFRICAAM 241A Gentrification
- AFRICAAM 121N Literature & Global Health
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DAAAS x Ujamaa House Spring Speaker Series featuring Fatoumata Seck
Date
Thursday May 29th 2025, 5:00 - 6:30pm
Event Sponsor
African & African American Studies
Location
Ujamaa A
326 Santa Teresa Street, Ujamaa A, Stanford, CA 94305
326 Santa Teresa Street, Ujamaa A, Stanford, CA 94305
Join us the Department of African & African American Studies (DAAAS) at Ujamaa House for an enlightening series of talks this spring brought to you in partnership with Ujamaa House. Don't miss out on the chance to connect with Black Stanford faculty, delve into their impactful research, and engage in meaningful discussions!
- Location: Ujamaa House
- Time: 5:00-6:30 PM
- Dinner will be provided!
Spring Speaker Series Schedule:
- Thursday, April 10 | Dr. Jordan Starck - "Diversity & The Perpetuation of Racial Inequality in the U.S."
- Thursday, May 1 | Dr. Alyce Adams - "Policy Strategies for Addressing Disparities in Chronic Disease Treatment Outcomes: Whe Access Isn't Enough"
- Thursday, May 29 | Fatoumata Seck - "African Revolutions"
This series is a perfect opportunity to network, foster an intellectual and academic environment, and grow together. Everyone is welcome!
Event Link
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Audio Library
Clayborn Carson
Interviewer: Umniya Najaer, Ph.D. Candidate in Modern Thought and Literature.
Interviewee: Dr. Clayborn Carson, Martin Luther King, Jr., Centennial Professor of History, emeritus, at Stanford University.
Date: May 9, 2023.
Location: Virtual
John Russell Rickford
Interviewer: Umniya Najaer, Ph.D. Candidate in Modern Thought and Literature.
Interviewee: Dr. John Russel Rickford, J.E. Wallace Sterling Professor of Linguistics, Emeritus, at Stanford University.
Date: May 8th May 19th, May 22nd, 2023.
Location: Stanford University
Bio: Dr. John Russel Rickford was born in 1949 in Guyana, the youngest of 10 siblings. Dr. Rickford earned his BA from UC Santa Cruz in 1971, and earned a PhD in Linguistics from UPenn in 1979. In 1980 he joined the Stanford faculty, where he taught for 40 years, until his retirement in 2019. Professor Rickford is renowned for his work on African American Vernacular English (AAVE) and research on pidgin and creole languages, especially, Guyanese Creole, Jamaican and Barbadian/Bajan. He has published 16 books on linguistics, including Spoken Soul: The Story of Black English, won the 2000 American book award.
Transcript of Clayborn Carson Audio
Transcript of John Russell Rickford Audio
Sandra Drake on St. Clair Drake
Interviewer: Umniya Najaer, Ph.D. Candidate in Modern Thought and Literature.
Interviewee: Dr. Sandra Drake, Professor of English, Emeritus at Stanford University.
Date: May 19, 2023.
Location: Stanford University
Bio: Today I have the pleasure of speaking with Professor Sandra Drake about her father, St. Clair Drake. Dr. St. Claire Drake was born on January 2, 1911 in Suffolk Virginia. His father was from Barbados and his mom from Virginia. He earned his BA in Biology from Hampton University and a PhD in social anthropology from the University of Chicago. A few of his most notable works are Black Metropolis: A Study of Negro Life in a Northern City, co-authored with Horace R. Clayton Jr published in 1945 and considered to this day a landmark study in Urban Studies. He taught Sociology at Roosevelt University from 1946-1968. The following year (1969) Drake founded the African and African American Studies program at Stanford, where he taught until his retirement in 1976. St. Claire Drake transitioned from earth on June 15, 1990 and continues to be widely celebrated. Both the AAAS Program at Stanford and the African American Studies Department at UC Berkeley have annual lectures named after St. Claire Drake to honor his contributions.
Sandra Drake
Interviewer: Umniya Najaer, Ph.D. Candidate in Modern Thought and Literature.
Interviewee: Dr. Sandra Drake, Professor of English, Emeritus at Stanford University.
Date: May 19, 2023.
Location: Stanford University
Bio: Dr. Sandra Drake was born in 1946 in Chicago and spent her childhood between the US, Ghana, and the United Kingdom. Dr. Drake earned her undergraduate degree in anthropology from Stanford in 1966 and a master’s in 1973 and a PhD in comparative literature in 1977. Professor Sandra Drake taught African American Studies and Literature in the English department until her retirement in 2004. Her publications include the critical study Wilson Harris and the Modern Tradition: A New Architecture of the World, the novel A Kind of Wrath, and the paper “All That Foolishness/That All Foolishness: Race and Caribbean Culture as Thematics of Liberation in Jean Rhys’ Wide Sargasso Sea."
Transcript of Sandra Drake on St. Clair Drake Audio
Transcript of Sandra Drake Audio
Ewart Thomas
Interviewer: Umniya Najaer, Ph.D. Candidate in Modern Thought and Literature.
Interviewee: Dr. Ewart Thomas, Professor of Psychology, Emeritus at Stanford University.
Date: May 15, 2023.
Location: Virtual
Bio: Dr. Ewart Thomas was born in 1942 in Guyana. He was the only boy amongst his six sisters. Dr. Thomas earned his BA in Mathematics from University of the West Indies, Jamaica. He went on to earn a PhD in Statistics from the University of Cambridge in 1967. In 1972 he joined the Stanford faculty, where he taught until his retirement in {2017}. During his tenure at Stanford, he earned a LLD from the University of the West Indies ( LLD is an honorary law degree of the highest qualification awarded to individuals for contributions of particular excellence.) Professor Thomas is renowned for his work in psychology, statistical methods, and theoretical and experimental analyses of information processing, equity, and of small-group processes.
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Brittany Linus, Class of 2024
Majoring in AAAS has allowed me to critically engage with the question: "What is Black technology?" Through class discussions, community engagement projects, and academic research, I explored Black interactions with and contributions to the development of digital technology, ecosystems, and the fandoms that thrive because of them. AAAS courses honed my understanding of Black digital rhetoric, teaching me to appreciate how every method of digital expression can declare, affirm, and protect one's Blackness. These explorations culminated in my creative honors thesis: a multidimensional exploration of the impact Black video gameplay styles have on digital landscapes. The greatest gift AAAS has given me is its holistic support—nurturing my multiplicity as a scholar, artist, and gamer. Through AAAS, I learned that knowledge is not just an institutional product, but a personal and communal process of reaching toward oneself to connect with others more intimately.
-Brittany
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DAAAS Team
Faculty
African and African American Studies • History Department
Professor of History and, by courtesy, of Religious Studies and of African and African American Studies
African and African American Studies • History
Michelle Mercer and Bruce Golden Family Professor of Feminist and Gender Studies, Professor of History and of African and African American Studies
African and African American Studies • English, African and African American Studies • African and African American Studies
Humanities and Sciences Professor, Professor of English and of African and African American Studies
African and African American Studies • Religious Studies
Martin Luther King, Jr. Centennial Professor and Professor of Religious Studies and of African and African American Studies
African and African American Studies • Classics, African and African American Studies • African and African American Studies
Associate Professor of Classics, of African and African American Studies and, by courtesy, of Comparative Literature
Department Chair
African and African American Studies • English, African and African American Studies • African and African American Studies
Jean G. and Morris M. Doyle Professor of Interdisciplinary Studies, Professor of English and of African and African American Studies and, by courtesy, of Comparative Literature
African and African American Studies • English, African and African American Studies • African and African American Studies
Associate Professor of English and of African and African American Studies
Courtesy
Professor of Education and, by courtesy, of African and African American Studies
Professor of Theater and Performance Studies and, by courtesy, of African and African American Studies
William Robertson Coe Professor in the Humanities Senior Fellow, Institute for Human-Centered Artificial Intelligence Bass University Fellow in Undergraduate Education
Joseph S. Atha Professor of Humanities and Professor, by courtesy, of African and African American Studies
Danily C. and Laura Louise Bell Professor of the Humanities and Professor, by courtesy, of African and African American Studies and of Iberian and Latin American Cultures
Staff
Core Curriculum Coordinator
Director of Advanced Studies and Community Engaged Learning, African and African American Studies
Communications and Events Coordinator, African and African American Studies
Student Svcs Offcr 1, African and African American Studies
Director of Finance and Operations, African and African American Studies
Faculty Affairs Coordinator, African and African American Studies
Lecturers
Stanford University Provostial Fellow/Lecturer
Lecturer
Graduate Teaching Affiliates
PhD Student in English at Stanford School of Humanities and Sciences
Ph.D. Student in History, admitted Autumn 2019, SHI Discussion Leader, Stanford Pre-Collegiate Studies
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Life After DAAAS
African & African American Studies majors pursue a variety of professional pathways. Here are some of our lovely alumni.
Featured Alumni
Sequoiah Hippolyte, Class of 2022
Creative Development Assistant at Sunhaus
Los Angeles, CA
Kory Gains, Class of 2021
Doctoral Student in Political Science, John Hopkins University
Baltimore, MD
Di'Vennci Lucas, Class of 2017
Law Enforcement Response Team Analyst at Facebook
Austin, TX
Gabriella Johnson, Class of 2017
Policy Analyst at Acumen LLC
Washington, D.C.
Jessie Schrantz, Class of 2017
Organizational Advancement Program Assistant at Be Strong Families
Chicago, IL
Kristen Powers, Class of 2016
Advocacy Coordinator, Southern Coalition for Social Justice
Saxapahaw, NC
Hope Burke, Class of 2014
Community Manager at Thread
Baltimore, Maryland
Farris Blount, Class of 2014
PhD Student at Boston University School of Theology studying Practical Theology
Boston, MA
Garry Mitchell, Class of 2013
PhD Candidate in Education at Harvard Graduate School of Education
Boston, MA
Shamika Goddard, Class of 2011
Master of Divinity concentrating in Technology and Ethics from Union Theological Seminary
Brooklyn, New York
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Peer Advisors
Billy Meneses
Hi! My name is Billy Meneses and I am a junior studying African and African American Studies and Public Policy with a concentration in Discrimination, Crime, and Poverty Policy. I am originally from Union City, NJ but I currently live in Northeastern Pennsylvania. Some of my research interests are histories of enslavement, Afro-Latinx identity and history, and studying the civil rights movement. I am also interested in civil rights law and social and economic mobility policy. Fun Fact: I used to be on a ballroom dance team in middle school and I was on Los Salseros, the salsa dance team here at Stanford!
Email: bmeneses [at] stanford.edu (bmeneses[at]stanford[dot]edu)
Ellie Alexander
Ellie Alexander (she/her) is a senior receiving her bachelor’s in African and African American Studies, with minors in Education and Sociology. She is also pursuing a coterminal master’s degree in Public Policy. A native of the DC-Maryland area, Ellie has a passion for learning about the historical inequities in the education system and discovering ways to better serve Black students and students with disabilities. In her free time, Ellie enjoys watching documentaries, baking, and doing jigsaw puzzles!
Email: elliea25 [at] stanford.edu
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