Principal AI Research Manager - Health & Life Sciences at Microsoft
Tel-Aviv, Tel-Aviv District, Israel
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Summary
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Aaron Bornstein is a Principal AI Research Manager specializing in Health & Life Sciences with over a decade of experience building NLP-driven healthcare technologies at Microsoft. He blends deep technical leadership with developer advocacy experience from Lightning AI, where he led community initiatives and produced technical content for PyTorch Lightning and Grid.ai. Aaron contributes to open-source AI tooling — notably improving documentation for Lightning Flash to make complex multi-domain ML recipes more accessible — reflecting a practical focus on usability and reproducibility. His background spans applied biomedical engineering work at NIH and secure healthcare systems development, giving him uncommon fluency across clinical workflows, research tooling, and production ML. Based in Tel Aviv, he pairs academic training in computer science with a history/humanities perspective that informs pragmatic, user-centered AI solutions.
12 years of coding experience
10 years of employment as a software developer
Winter Session, Germany In The New Europe, Winter Session, Germany In The New Europe at University of Tübingen
Bachelor of Arts (B.A.), Cum Laude, Honors in History, Honors in Computer Science, Bachelor of Arts (B.A.), Cum Laude, Honors in History, Honors in Computer Science at Goucher College
Masters of Science (MSc), Computer Science, Masters of Science (MSc), Computer Science at Bar-Ilan University
OSP, Computer Science, History, OSP, Computer Science, History at Ben-Gurion University of the Negev
Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains
Role in this project:
Technical Writer
Contributions:23 reviews, 11 commits, 18 PRs in 6 months
Contributions summary:Aaron primarily contributed to the repository's documentation, focusing on refining existing guides and examples. Their work included updating documentation for image classification and tabular classification tasks, as well as general documentation improvements regarding data flow and predictions. The user also addressed typos and clarified concepts to improve accessibility for users with varying levels of machine learning experience.
Contributions:19 commits, 15 pushes, 2 comments in 13 days
teachermathpythonmicrosoft
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