Max Simchowitz

Max Simchowitz started his academic journey as a math major at Princeton University, and then went on to do a Ph.D. focusing on theoretical questions in machine learning in the EECS department at UC Berkeley.

Human beings possess a remarkable ability to learn quickly, consistently, resourcefully and in an manner that generalizes to novel or unseen scenarios. Simchowitz's lab is interested in how to make AI do the same. By carefully understanding intersecting principles from control theory, reinforcement learning, deep learning, generative modeling, and optimization, his lab is exploring questions such as:

• Why do today’s robotic policies work so well, and how can we make them better?
• How can robots learn efficiently, without the pain of task-specific, engineering-intensive hyperparameter tuning? and
• How can we design new optimization algorithms targeted for AI systems that interact with–and can be led astray by–their environment?