Jesse Zhang is a robotics and reinforcement learning researcher with a decade of experience building agents that learn new tasks autonomously in real-world settings. Currently a postdoc at the University of Washington and research collaborator at AI2, he bridges cutting‑edge academic work with industry internships at NVIDIA and AWS. His PhD work at USC and prior projects at Berkeley produced publications on offline RL, safe adaptation, and reproducible manipulation benchmarks accepted to venues like ICLR, ICML, CoRL, and ICRA. Jesse has a track record of designing hierarchical and intrinsic-motivation methods for long-horizon, sparse-reward problems and has applied those ideas across both simulated and low-cost real-robot platforms. Based in Bellevue, WA, he combines strong teaching and mentorship experience with hands-on systems-building from his undergraduate research through to production-oriented research internships. Colleagues know him for turning theoretical insights into practical algorithms that accelerate robot learning outside the lab.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Southern California
Bachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at University of California, Berkeley
Contributions:28 commits, 14 pushes, 5 comments in 11 months
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