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
Dr. David Fridovich-Keil directs the Control and Learning for Autonomous Robotics (CLeAR) Laboratory, and is a core member of the Oden Institute for Computational Engineering and Sciences and Texas Robotics.
He received his B.S.E. in Electrical Engineering from Princeton University and his Ph.D. in Electrical Engineering & Computer Sciences from the University of California, Berkeley. Fridovich-Keil’s research spans optimization, game theory, machine learning, and robotics, with a substantial focus on establishing game-theoretic models of multi-agent strategic interactions, inverting those models to infer agents’ intentions from data, and leveraging that information to guide future interactions. A key aim of Fridovich-Keil’s recent work has been to integrate these capabilities with generative machine learning techniques by leveraging fundamental connections with optimization theory and differentiable programming. Fridovich-Keil is the recipient of an NSF Graduate Research Fellowship and an NSF CAREER award.
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
- Hi! PARIS Starting Career Chair
- Frontiers of Engineering Symposium of the National Academy of Engineering
- Peter O’Donnell, Jr. Distinguished Research Award