Control and Learning for Autonomous Robotics Group


We are a group of scientists and engineers working at the intersection between robotics, control theory, machine learning, and game theory to design high performance, interactive autonomous systems.

  • Brett Barkley

    Brett Barkley

    PhD Student

    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 Headshot

    David Fridovich-Keil

    Assistant Professor

    Principal Investigator of Control and Learning for Autonomous Robotics

  • Antonio Lopez Guzman

    Antonio Lopez Guzman

    Master’s Student

    Antonio Lopez is a Fulbright scholar from Mexico pursuing a Master’s degree at University of Texas at Austin. His interests include exploring the use of optimal control, control theroy and machine learning on autonomous systems, robot safety and spacecraft applications. Antonio completed his BS in Mechatronics at National Autonomous University of Mexico (UNAM) where he worked in two nanosatellite projects called K’OTO and KuauhtliSat. In his free time, Antonio enjoys playing chess, hiking and visit museums.

  • Junette Hsin

    Junette Hsin

    Master’s and PhD student

    Junette Hsin is a Master’s and PhD student at the University of Texas at Austin. Junette’s research interests span orbital mechanics, robotics, Koopman operators, control theory, and estimation. After graduating from UC Davis with a Bachelors in Aerospace and Mechanical Engineering, Junette worked for 4 years at Maxar Technologies (formerly Space Systems/Loral). Her previous roles include Mass Properties Engineer and Flight Engineer in the Mission Control Center, and she is currently still working there as a Dynamics and Controls Analyst while completing her Master’s degree.

  • Hamzah Khan

    Hamzah Khan

    Master’s and Ph.D. Student

    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).

  • Jacob Levy

    Jacob Levy

    Master’s and PhD Student

    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.

  • Fernando Palafox

    Fernando Palafox

    PhD Student

    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.

  • Jonathan Salfity

    Jonathan Salfity

    PhD Student

    Jonathan Salfity is a PhD student at the University of Texas at Austin, primarily advised by Mitch Pryor in the Nuclear and Applied Robotics Group. Jonathan’s research covers robotics, control theory, learning for control, machine learning, reinforcement learning, and dynamic game theory. His research north star is to blend the best outcomes of dynamics and control theory – safety guaranteees, robustness, sensitivity, stability – into emerging learned-based algorithms for safe, robust autonomous systems.

    Prior to enrolling at UT Austin, Jonathan worked for 4 years at HP Labs in Palo Alto, CA, where his research focused on indoor mobile robots, robotic sensing and manipulation for post-processing of 3D printed parts, and reinforcement learning. Prior to HP Labs, Jonathan worked for 2 years in HP 3D-Print R&D on low-level control engineering in San Diego, CA. Jonathan completed his M.S. and B.S. in Mechanical Engineering at UCLA.