Assistant Professor
Principal Investigator of Control and Learning for Autonomous Robotics
David Fridovich-Keil is an assistant professor at the University of Texas at Austin. Dr. Fridovich-Keil's research spans optimal control, dynamic game theory, learning for control, and robot safety. While he has also worked on problems in distributed control, reinforcement learning, and active search, he is currently investigating the role of dynamic game theory in multi-agent interactive settings such as traffic. Dr. Fridovich-Keil's work also focuses on the interplay between machine learning and classical ideas from robust, adaptive, and geometric control theory. Dr. Fridovich-Keil completed his PhD under the supervision of Claire Tomlin at UC Berkeley and did a postdoc at Stanford University with Mac Schwager. During his PhD, Dr. Fridovich-Keil interned at Nuro, where he worked on motion planning and prediction. He is the recipient of an NSF Graduate Research Fellowship and an NSF CAREER Award.