Garrett Thomas is a PhD candidate in Computer Science based in the Bay Area with 11 years of hands-on experience spanning research internships and teaching roles. He specializes in reinforcement learning and robotics, having built transformer-based architectures for manipulation (work presented at CoRL) and explored human-preference and offline RL at Nuro and ByteDance. A Berkeley-trained mathematician and former CS instructor, he brings rare experience translating classroom pedagogy into reproducible research and practical system design. Garrett’s background ranges from web and API development to large-scale ML research, highlighting an ability to move between production engineering and cutting-edge algorithmic work. Notably, he has collaborated with leading labs and industry teams to integrate diverse data sources and human feedback into decision-making systems.
11 years of coding experience
2 years of employment as a software developer
Bachelor's Degree Mathematics and Computer Science, Bachelor's Degree Mathematics and Computer Science at University of California, Berkeley
Palos Verdes Peninsula High School
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Stanford University
Notes on mathematical topics that pertain to machine learning
Contributions:6 commits, 5 pushes, 1 branch in 1 year
mathematicalnotespythonmachine-learning
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