Max Simchowitz

Max Simchowitz 

Max Simchowitz
msimchow(AT)berkeley(DOT)edu
CV
Google Scholar profile

About Me

I am a rising sixth year PhD student in the EECS department at UC Berkeley, co-adivsed by Ben Recht and Michael Jordan. I am currently supported by an Open Philanthropy fellowship, and in the past have received support form an NSF GRFP grant and a Berkeley Fellowship. 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). My recent work has focused on the theoretical foundations of online linear 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 particularly interested in transferring optimal “fast rates” familiar in statistical and online learning to more complex reinforcement learning settings, and, conversely, understanding when these improved bounds are unattainable. I'm also interested in characterizing optimal regret when the learner faces additional challenges, such as incomplete observation, corrupted information, and safety constraints.

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.