Jonathan Hayase is a CS PhD student at the University of Washington and a graduate research assistant with 12 years of engineering and research experience focused on security and privacy for machine learning systems. He has combined industry and academic roles—student researcher at Google, AI Fellow at Sentient Labs, and multiple internships at Pure Storage and UnifyID—working on privacy for large language models and sensor-driven ML. His research record includes theoretical work on learning distributions and a paper at AJC AI, plus novel algorithms for automata inference, reflecting strong theory-to-system fluency. He’s comfortable shipping low-level, production-grade C/C++ systems for constrained hardware (satellite and embedded IoT) as well as high-level ML prototypes in Python and Julia. Based in Seattle, he brings interdisciplinary breadth from physics and mathematics to applied ML security, often bridging rigorous proofs with pragmatic engineering. A less obvious strength is his track record of moving between prototype hardware projects and formal theoretical results, enabling impactful, well-rounded contributions to trustworthy AI.
12 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at University of Washington
Mathematics, Mathematics at Cypress College
Bachelor of Science (B.S.), Joint Computer Science and Mathematics, Senior, Bachelor of Science (B.S.), Joint Computer Science and Mathematics, Senior at Harvey Mudd College
Contributions:27 pushes, 1 branch in 6 years 9 months
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