resources/resume.txt

Name: Teja Krishna Cherukuri
Phone: 470-662-7146
Address: Atlanta, GA (Open to Relocation)
Email: tejakrishnacherukuri@gmail.com
Linkedin Url: https://linkedin.com/in/tejacherukuri
Github Url: https://github.com/tejacherukuri
Portfolio Url: https://tejacherukuri.github.io
Google Scholar Url: https://scholar.google.com/citations?user=6S9WmqwAAAAJ&hl=en

Education
College: Georgia State University
Location: Atlanta, GA
Degree: Master of Science in Computer Science, GPA: 4.21/4.3
Duration: Aug 2023 – May 2025
Coursework: Deep Learning, Advanced Machine Learning, Computer Vision, Natural Language Processing, Digital
Image Processing, Computational Intelligence, Data Science

Technical Skills
Languages: Python, Java, SQL
Frameworks: PyTorch, TensorFlow, Keras, Flask, FastAPI, LangChain, Streamlit
Libraries: NumPy, Pandas, Scikit-Learn, Matplotlib, OpenCV, NLTK
Cloud & DevOps Tools: Git, Docker, Azure ML Studio, Azure AI Services

Work Experience
Role: Graduate Research Assistant
Company: Georgia State University (TReNDS Lab)
Duration: Sep 2023 – Present
Location: Atlanta, GA

Responsibilities or duties:
• Published 5 IEEE papers showcasing the impact of our research methods for advancing AI in Medicine.
• Developed and fine-tuned Multi-modal LLMs for medical image captioning using PyTorch, integrating images
with diagnostic text, achieving a 13.4% higher BLEU4 over VisionGPT, with just 440M parameters, and
reducing inference time to 1.6 sec per image.
• Designed various medical image classification models for diagnosing chronic diseases such as schizophrenia, diabetic
retinopathy, breast cancer, and colon cancer using specific imaging modalities with 5%–7% lower false negatives.
• Enabled high-performance computing for training deep learning models through Slurm job scheduling,
optimizing resource allocation and accelerating processing times.

---Below are more details about the work I did, in the GRA role, can be used more in the context of research positions if needed---
More details starts here
Note: The below is just a background info of my works, you can only use this when there is absolute need of drafting to detail and stuff. 
• Medical Vision Language Transformer: Pioneered a novel approach for resource-constrained environments, integrating Abstractor & Adaptor to enhance feature focus and fusion, achieving expert-level precision in medical image captioning.
• Multi-Modal Medical Transformer: Devised a vision-language model integrating retinal image features & clinical keywords, achieving a 13.5% improvement in BLEU-4 over GPT-2 for accurate diagnostic report generation and improving explainability by visualizing attention to diseased regions.
• Guided Context Gating: Innovated a novel attention model to improve context learning in retinal images, boosting accuracy by 2.63% over advanced attention methods & 6.53% over Vision Transformer, enhancing retinopathy diagnosis.
• Spatial Sequence Attention Network: Formulated a unique attention mechanism to identify Schizophrenia specific regions in brain sMRI, improving diagnosis accuracy by 6.52% and clinical interpretability with neuroanatomical insights.
• Multi-Modal Imaging Genomics Transformer: Designed a fusion model combining genomics with sMRI & fMRI, bettering Schizophrenia diagnosis accuracy by 2.12% and revealing associated genetic markers.
More details ends here

Role: Data Scientist
Company: Tata Consultancy Services Limited
Duration: Nov 2020 – Aug 2023
Location: Hyderabad, TS

Responsibilities or duties:
• Built a customer attrition system based on ensemble of SVM, Random Forest, and AdaBoost in Python using
scikit-learn, improving 42 basis points in annual customer retention.
• Led a POC for a dynamic risk-based pricing model, aligning interest rates with borrower risk profiles and market
conditions, which reduced underpriced loans by 18%, and generated $3M in annual revenue growth.
• Implemented REST APIs using FastAPI to surface machine learning models for loan approval and fraud
detection, reducing workflows processing time by 30% and preventing potential fraud losses of $25M, annually.
• Developed and deployed a chatbot using Azure AI Bot Service for handling customer queries in collaboration
with the Customer Experience & Personalization team, achieving a 96% CSI and a 3.5 FTE reduction.
• Achieved sub-100ms response times for high-volume inference requests by containerizing models with Docker and
deploying them on GPU-enabled Azure Container Instances.

Research Experience and Accomplishments
• Published 7 research papers in journals, with 280+ citations and 5 H-index, pioneering Attention models,
Multi-modal learning, Transformers and Large Vision Language Models.
• Presented 5 works at reputed conferences, including ISBI 2024, ICIP 2024, ISBI 2025, and ICASSP 2025.

Projects
Name: RetinAI Doctor
Link: https://github.com/TejaCherukuri/Guided-Context-Gating
Demo: https://huggingface.co/spaces/tejacherukuri/Guided-Context-Gating
Technologies used: Python, Streamlit, TensorFlow, OpenCV, Git
Duration: Feb 2024 - Jun 2024
• Built an AI tool to process retinal scans, predict diabetic retinopathy (DR) severity, and achieved 90.13% accuracy
and recall using a novel Guided Context Gating (GCG) attention mechanism.
• Enhanced interpretability by generating attention maps that highlight areas of focus, empowering ophthalmologists
with insights for early and reliable DR diagnosis.


Additional details about my resume: 
Job Interests: Data Scientist, Applied Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Deep Learning Engineer, Research Engineer
Open to any location to work within the U.S (Comfortable with all modes of working - hybrid, onsite and remote)
I can only apply to jobs with (university grad roles, early career postings, associate level postings, 3 years of experienced roles)
I have end-to-end machine learning project experience, from requirement gathering, model development, model optimisation, model evaluation and model deployment. 
Additional Skills: 
Machine Learning, Linear Regression, Logistic Regression, Classification, PCA, Ensembling.
Deep Learning: Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, LLMs, MLLMs, Vision Language Models, Generative AI, VAE.
Certification: Deep Learning Specialisation from DeepLearning.AI, Python for Everybody certification from University of Michigan