people
Faculty
Negar Mehr
Negar is an assistant professor in the Mechanical Engineering department at UC Berkeley. The focus of her research is to develop control algorithms that allow autonomous systems to safely and intelligently interact with each other and with humans.
Postdocs
Jean-Baptiste Bouvier
Jean-Baptiste is a postdoctoral scholar in the Department of Mechanical Engineering at UC Berkeley. He is interested in guaranteeing the resilience of autonomous systems by leveraging reinforcement learning, control theory, machine learning and game theory. Jean-Baptiste completed his PhD in Aerospace Engineering at the Univervsity of Illinois Urbana-Champaign (UIUC) in 2023 where he developed analytical resilience theory. Prior to this, he received a dual MSc degree in Aerospace Engineering from UIUC and ISAE Supaéro in France where his research focused on astrodynamics and optimal control.
Graduate Students
Maulik Bhatt
Maulik is a graduate student in the Mechanical Engineering department at UC Berkeley. He is interested in leveraging game theory, stochastic control, and machine learning to enable robotic multi-agent interactions and safe motion planning. Previously, he graduated from IIT Bombay with an interdisciplinary dual degree. During his undergraduate, he worked in the area of state estimation using geometric control and variational principle.
Kanghyun Ryu
Kanghyun is a graduate student in the Mechanical Engineering department at UC Berkeley. He received his B.S in Aerospace Engineering at Seoul National University in 2022. His current research interests lie in optimal control, machine learning, robotics, and their intersection for safe learning and operation of autonomous systems.
Kartik Nagpal
Kartik is a PhD Student in Mechanical Engineering at UC Berkeley. He recently graduated with a B.S. in Computational Engineering from University of Texas at Austin, where he developed a background in Optimization, Controls, and Orbital Mechanics. Now, his research is focused on safety for robotic systems and multi-agent reinforcement learning problems. He is particularly interested in elucidating the black box models of machine learning to more meaningully and safely apply these method to complex real world systems.