Max Simchowitz
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Max Simchowitz |
About Me
I am a postdoc in Russ Tedrake's group at MIT. My recent work has focused on the theoretical foundations of online control and reinforcement learning, with past research ranging broadly across topics in adaptive sampling, multi-arm bandits, complexity of convex and non-convex optimization, and fairness in machine learning. I am currently interested in developing rigorous, theoretical guarantees for nonlinear control, wherever possible.
I received my PhD student in the EECS department at UC Berkeley, co-adivsed by Ben Recht and Michael Jordan, where I was generously supported by Open Philanthropy, NSF GRFP grant and Berkeley Fellowship grants. Previously, I recieved a BA in Mathematics at Princeton University, where I was fortunate enough to do research with Sanjeev Arora and David Blei (who taught at Princeton at the time).
Teaching
CS 189/289A, Introduction to Machine Learning, UC Berkeley Fall 2018 (TA).
EE227C, Convex Optimization and Approximation, UC Berkeley, Spring 2018 (TA). Link for course notes.