Max Simchowitz

Max Simchowitz is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University, joining in January 2025. His research focuses on sequential learning, reinforcement learning, control systems, and robotics, with a particular interest in how large AI models influence these fields. He holds a PhD from UC Berkeley (2021) and conducted postdoctoral research in MIT's Robot Locomotion Group. His work bridges theoretical foundations and practical applications, emphasizing adaptive sampling, optimization, and fairness in machine learning.

Education:

• Bachelor's in Mathematics, Princeton University (2015)
• PhD in EECS, UC Berkeley (2021), advised by Ben Recht and Michael Jordan

Research Interests:

• Reinforcement learning and control systems
• Generative models (diffusion models, video prediction)
• Robot learning and policy optimization
• Mathematical foundations of sequential decision-making

Articles Trends: Recent work emphasizes diffusion models, imitation learning pitfalls, and robot policy optimization. Earlier contributions include theoretical analyses of system identification, exploration strategies, and fairness in AI systems.

Awards:

• Outstanding Paper Award (ICML 2022)
• Best Paper Finalist (ICRA 2024)
• Best Paper Award (ICML 2018)

Advising & Grants: Actively recruiting PhD/Master’s students in CMU’s Machine Learning Department and Robotics Institute. Prior teaching includes UC Berkeley’s Convex Optimization and Machine Learning courses. Research supported by grants exploring robot learning, generative models, and control theory.