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.

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