Yash Sharma
Director Of Research at Neurometric AI
New York City Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolYash Sharma is a research-driven AI leader with 11 years of experience bridging deep learning research and product-focused ML engineering, currently directing research at Neurometric AI in the New York area. He completed a PhD at the International Max Planck Research School for Intelligent Systems under prominent advisors and has internships and research stints at Meta FAIR, Google Brain, AWS, and Flagship Pioneering. Yash has hands-on expertise in adversarial robustness—contributing meaningful enhancements to the widely used cleverhans adversarial example library—and a track record of spinning research into startups, including founding a stealth company after an Entrepreneurs First residency. He combines rigorous academic training in machine learning and electrical/computer engineering with practical applied science experience, routinely moving ideas from code and papers into production-relevant systems.
11 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at University of TübingenBachelor & Master of Engineering Electrical Engineering (Computer Engineering Track), Bachelor & Master of Engineering Electrical Engineering (Computer Engineering Track) at The Cooper Union for the Advancement of Science and Art
Hindi, Gujarati, English
Github Skills (8)
machine-learning10
adversarial-attacks10
tensorflow10
python10
security10
benchmark9
benchmarking9
keras6
Programming languages (3)
Github contributions (5)
cleverhans-lab/cleverhans
Sep 2017 - Aug 2018
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Role in this project:
ML Engineer
ML EngineerContributions:22 commits, 21 PRs, 25 comments in 10 months
Contributions summary:Yash primarily contributed to the `cleverhans-lab/cleverhans` repository, focusing on the implementation and modification of adversarial attack methods. Their work involved developing and refining the ElasticNetMethod, adding options for decision rules and optimizers. These changes involved modifying core attack files and incorporating new testing functionalities, likely improving the robustness and functionality of the adversarial example generation.
benchmarkingrobustnessadversarial-machine-learningsecurityadversarial
ysharma1126/EAD_Attack
Sep 2017 - Sep 2018
Contributions:2 releases, 49 commits, 53 pushes in 1 year
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