DavidFridovich-Keil
- dfk@utexas.edu
- ASE 3.232, POB 5.320
Optimal control theory; Dynamic game theory; Motion planning; Multi-agent decision making; Machine learning and control
About
David 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. Fridovich-Keil’s work also focuses on the interplay between machine learning and classical ideas from robust, adaptive, and geometric control theory.
Fridovich-Keil joined the department as an assistant professor in Fall 2021. He received his doctorate from the University of California, Berkeley, where he developed some of the first efficient techniques for solving noncooperative, game-theoretic motion planning problems. During his graduate studies, Fridovich-Keil briefly worked at the self-driving car company Nuro. His postdoctoral research focused on exploiting computational parallelism in stochastic optimal control problems.
Educational Qualifications
Ph.D., Electrical Engineering and Computer Science, University of California, Berkeley
B.S.E., Electrical Engineering, Princeton University
Select Awards & Honors
- National Science Foundation CAREER Award, 2024