Max Simchowitz

Max Simchowitz 

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

About Me

I am an incoming assistant professor in the Machine Learning Department at Carnegie Mellon University, beginning in January 2025. I will be spending this Fall as a visiting researchers at the Modern Paradigms in Generalization program at the Simons Institute for the Theory of Computing, at UC Berkeley. As such, I am unfortunately not looking to take students this cycle (but remain open to co-advising and other collaborations!).

My current work focuses on the theoretical foundations of learning for robotics and how to operationalize these insights in practical algorithms; this research has motivated a parallel interest in learning and extrapolation under distribution shift. My current work draws on past research ranging broadly across topics in adaptive sampling, multi-arm bandits, complexity of convex and non-convex optimization, reinforcement learning, learning in linear and nonlinear dynamical systems, and fairness in machine learning.

I was formerly a postdoc in Russ Tedrake's Robot Locomotion Group in the EECS Department at MIT. I received my PhD in the EECS department at UC Berkeley, co-advised 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).

Selected Works

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.