Control and Learning for Autonomous Robotics Group

Brett Barkley is a Computer Science PhD student at the University of Texas at Austin co-advised by David Fridovich-Keil and Amy Zhang. Brett’s research interest focuses on methods that promote waste minimization in the lifecycle of deep reinforcement learning algorithms, specifically the 3 Rs: reduce, reuse, recycle.

Before enrolling at UT, Brett was an employee of the Johns Hopkins Applied Physics Laboratory where he was the sub and full-scale aircraft red team autonomy lead for DARPA ACE. Brett holds a BS and MS in Aerospace Engineering from the University of Maryland and a BS in Engineering Physics from Elon University. Outside of research, Brett enjoys being a hobbyist in brazilian jiu jitsu, playing video games with friends, and eating entirely too much H-E-B queso.

David Fridovich-Keil is an assistant professor at the University of Texas at Austin. David’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. David’s work also focuses on the interplay between machine learning and classical ideas from robust, adaptive, and geometric control theory. David 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, David interned at Nuro, where he worked on motion planning and prediction. David is the recipient of an NSF Graduate Research Fellowship and an NSF CAREER Award.

Kushagra Gupta is a Ph.D. student in the Department of Electrical and Computer Engineering at UT Austin; his research interests lie at the intersection of control, learning and games for robotics. Prior to starting graduate studies, Kushagra earned his B. Tech. in Mechanical Engineering from the Indian Institute of Technology Delhi in 2023. He is co-advised by Dr. Ufuk Topcu, Dr. David Fridovich-Keil, and Dr. Sandeep Chinchali.

Jaehan Im is a Ph.D. student in the Department of Aerospace Engineering , co-advised by Dr. Ufuk Topcu and Dr. David Fridovich-Keil. He received his B.S. and M.S. degrees in Aerospace Engineering from KAIST, South Korea. His research focuses on decentralized and noncooperative coordination in multi-agent systems, air traffic management, and safety-critical human-autonomy interaction.

Hamzah Khan is a Master’s and Ph.D. student at the University of Texas at Austin in the Aerospace Engineering department and is advised by Professor David Fridovich-Keil. His interests span distributed control and planning, game theory, interpretability in learned systems, robot safety, and autonomous vehicles. He worked for three years in the self-driving vehicle industry at Uber ATG and subsequently, Aurora Innovation. Hamzah completed his undergraduate degree at Harvey Mudd College in Southern California (Class of 2018).

Dong Ho Lee is a PhD student at the University of Texas at Austin. Dong Ho’s research interests lie at the intersection between optimization, control theory and learning for autonomous multi-agent systems.

Prior to UT Austin, Dong Ho served as a First Lieutenant (Research Officer) in the ROK army for 3 years in Daejeon, South Korea. He received his B.S. and M.S. in Aerospace Engineering at the Korea Advanced Institute of Science and Technology (KAIST).

Jacob Levy is a Master’s and PhD student at the University of Texas at Austin. Jake is interested in advancing techniques in control theory and autonomy for unmanned spacecraft applications.

Prior to enrolling at UT Austin, Jake worked for 10 years at Parker Aerospace in Fort Worth, TX. His previous roles include Engineering Test Lab Manager and Test Engineer. Jake completed his B.S. in Aerospace Engineering at the University of Texas at Arlington.

Jingqi is a postdoctoral researcher at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin, advised by Professor David Fridovich-Keil. His research lies at the intersection of dynamic game theory, control, and reinforcement learning, where he develops theoretical and practical methods to advance agents’ strategic reasoning and enable coordination in complex, uncertain scenarios. His work spans applications such as autonomous driving, air mobility, multi-robot manipulation, and smart grids.

Prior to UT Austin, he earned his Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley, in 2025, advised by Professors Claire Tomlin and Somayeh Sojoudi.

Xinjie Liu is a Ph.D. student in the Department of Electrical and Computer Engineering at The University of Texas at Austin. Xinjie is very fortunate to be co-advised by Prof. Ufuk Topcu and Prof. David Fridovich-Keil. His research interests lie in developing theoretical foundations and practical algorithms for decision making and control in autonomous systems under uncertainty. He is currently focused on (i) efficient and scalable reinforcement learning through principled integration of data from multiple sources, and (ii) intelligent and safe interaction for multi-agent systems, such as robots operating in shared environments. The majority of his work draws on reinforcement learning, numerical optimization, game theory, and statistical estimation and inference.

Ronnie Ogden is a PhD student in the Department of Aerospace Engineering at UT Austin. He is currently researching how to leverage optics for real-time vision compression in robotics systems. His recent work utilizes the fields of stochastic control/estimation, information theory and physics.

Prior to UT, Ronnie was a flight test engineer at Wisk Aero in Mountain View, CA. He has a passion for improving autonomous flight systems and exploring new problems. Ronnie received his BS in Aerospace Engineering and Mathematics from the Massachusetts Institute of Technology. In his free time, he enjoys climbing, language learning and solving puzzles

Fernando Palafox is a PhD student at the University of Texas at Austin. Fernando is interested in understanding multi-agent autonomous systems through the lens of controls, game theory, and artificial intelligence. Fernando holds a BS and MS in Aerospace Engineering from the University of Colorado Boulder. Outside of research, he is a competitive cyclist and enjoys photography.

Tianyu Qiu is a first-year Ph.D. student in the Department of Aerospace Engineering and Engineering Mechanics, Cockrell School of Engineering at The University of Texas at Austin. He is advised by Prof. David Fridovich-Keil. His research interests lie in game theory, reinforcement learning and decision-making for multi-agent systems and robots.

Prior to UT Austin, Tianyu received his M.S. degree in Electronic Information from the Department of Automation, School of Electronic Information and Electrical Engineering at Shanghai Jiao Tong University. His master thesis was on Social Navigation for Mobile Robots based on Inverse Dynamic Games. Tianyu obtained his B.S. degree in Electrical and Computer Engineering from Shanghai Jiao Tong University.

David SeWell is a 1st year PHD student in ECE at UT Austin. His research interests broadly include empirical game theory, reinforcement learning, and operator learning. He is currently interested in taking an operator perspective to reinforcement learning problems.

David worked in industry for a number of years. Most recently he worked as a machine learning engineer for Lockheed Martin where he conducted research in empirical game theory as well as worked on building out a number of reinforcement learning based systems.

Yang Tan is a first-year Ph.D. student in the Computational Science, Engineering, and Mathematics (CSEM) program at the University of Texas at Austin. Her research interests include auction theory, game theory, computational optimization, and algorithmic design.