Max Cohen
Welcome! I'm a Postdoctoral Scholar at the California Institute of Technology where I work in the AMBER Lab with Professor Aaron Ames. Prior to joining Caltech, I earned my Ph.D. in Mechanical Engineering from Boston University working in the Boston University Robotics Lab with Professor Calin Belta and, prior to that, earned my B.S. in Mechanical Engineering from the University of Florida. As a graduate student, I was awarded a National Science Foundation Graduate Research Fellowship and won Best Dissertation in Mechanical Engineering from Boston University.
I am starting as a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University this fall and am currently recruiting Ph.D. students for Spring 2026 and Fall 2026. If you are interested in pursuing a Ph.D. in the general areas of control theory, robotics, and autonomy, please check out my prospective students page.
My research broadly focuses on control systems with applications in robotics and autonomy. I'm particularly interested in getting highly dynamic systems (e.g., walking robotics, drones, aircraft) to complete complex tasks. My current research focuses on safety-critical control architectures with the goal of certifying system-level properties of such systems based on properties of their individual components (e.g., controllers, motion planners, state estimators). I'm also interested in unifying classical learning-based control approaches, such as adaptive control, with more modern machine learning-based techniques to endow autonomous systems with adaptive capabilities. For more details on my research check out the videos below and my publications.
Recent News
Five Papers Accepted to CDC 2025!Attending the 2025 American Control Conference
Best Paper Award at the 2025 Conference on Learning for Dynamics and Control
Joining North Carolina State University as an Assistant Professor
all posts
Research Highlights
Selected Publications
Safety-Critical Control
Rectified Control Barrier Functions for High-Order Safety Constraints
Pio Ong, Max H. Cohen, Tamas G. Molnar, and Aaron D. Ames
IEEE Control Systems Letters / arXiv / bibTeX
Constructive Safety-Critical Control: Synthesizing Control Barrier Functions for Partially Feedback Linearizable Systems
Max H. Cohen, Ryan K. Cosner, and Aaron D. Ames
IEEE Control Systems Letters / arXiv / video / bibTeX
Characterizing Smooth Safety Filters via the Implicit Function Theorem
Max H. Cohen, Pio Ong, Gilbert Bahati, and Aaron D. Ames
IEEE Control Systems Letters / arXiv / bibTeX
Layered Architectures and Reduced-Order Models
Safety-Critical Controller Synthesis with Reduced-Order Models
Max H. Cohen, Noel Csomay-Shanklin, William D. Compton, Tamas G. Molnar, and Aaron D. Ames
American Control Conference / arXiv / video / bibTeX
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
William D. Compton, Max H. Cohen, and Aaron D. Ames
Learning for Dynamics and Control / arXiv / video / bibTeX
Safety-Critical Control for Autonomous Systems: Control Barrier Functions via Reduced-Order Models
Max H. Cohen, Tamas G. Molnar, and Aaron D. Ames
Annual Reviews in Control / arXiv / bibTeX
Adaptive Control and Reinforcement Learning
Adaptive and Learning-based Control of Safety-Critical Systems
Max H. Cohen and Calin Belta
Springer / bibTeX
Temporal Logic Guided Safe Model-based Reinforcement Learning: A Hybrid Systems Approach
Max H. Cohen, Zachary Serlin, Kevin Leahy, and Calin Belta
Nonlinear Analysis: Hybrid Systems / bibTeX
Safe Exploration in Model-based Reinforcement Learning Using Control Barrier Functions
Max H. Cohen and Calin Belta
Automatica / arXiv / bibTeX