Debajyoti Datta
Co-Founder Research at Hippocratic AI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDebajyoti Datta is a researcher-engineer and co-founder with 14 years of experience building large-scale machine learning systems across startups and AWS AI, specializing in few-shot learning, information extraction, and semantic understanding of images and text. He has a strong research pedigree—including a PhD in ML for healthcare/NLP and roles at CMU and Télécom ParisTech—and practical production experience scaling algorithms (e.g., spectral clustering at Crossing Minds that handled multi-terabyte problems). A winner of the PyTorch Summer Hackathon and recipient of the AllenAI NLP Impact Award, he also co-chairs BigScience’s Biomedical Working Group and was part of the US Future Leaders Summit for Responsible Data Science. His open-source contributions include implementing core meta-learning models in the widely used learn2learn PyTorch library, reflecting a blend of prototyping, debugging, and production-minded integration. Based in San Francisco, he combines deep academic background with hands-on engineering to translate cutting-edge ML research into scalable products.
13 years of coding experience
4 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceMaster's Degree Systems Engineering Machine Learning, Master's Degree Systems Engineering Machine Learning at University of Virginia
English, Bengali, Hindi
Github Skills (4)
pytorch10
meta-learning10
mask-rcnn9
faster-rcnn9
Programming languages (4)
Github contributions (5)
learnables/learn2learn
Aug 2019 - Sep 2019
A PyTorch Library for Meta-learning Research
Role in this project:
ML Engineer
ML EngineerContributions:27 commits, 11 PRs, 27 pushes in 1 month
Contributions summary:Debajyoti contributed to the implementation of various meta-learning models within the learn2learn library. Their work involved integrating and debugging Prototypical Networks, MAML, and other related functionalities, including the adaptation of convolutional neural networks (CNNs) for image-based tasks. The user focused on addressing dimensional errors and ensuring the proper functioning of these models within the repository's framework. Their efforts centered on prototyping, debugging, and integrating various meta-learning techniques.
pytorchmeta-learningmetalearningmeta-optimizationdeep-learning
debajyotidatta/biomedical
Sep 2021 - Dec 2022
Tools for curating biomedical training data for large-scale language modeling
Contributions:34 pushes, 5 branches in 1 year 2 months
machine-learningscaletraininglarge-scalemodeling-language
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