About Me

Academic Bio

Somil Bansal is an Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University. Before that, he was an assistant professor in the Electrical and Computer Engineering department at the University of Southern California, Los Angeles. He received a Ph.D. in Electrical Engineering and Computer Sciences (EECS) from the University of California at Berkeley in 2020. Before that, he obtained a B.Tech. in Electrical Engineering from the Indian Institute of Technology, Kanpur, and an M.S. in Electrical Engineering and Computer Sciences from UC Berkeley in 2012 and 2014, respectively. Between August 2020 and August 2021, he spent a year as a Research Scientist at Waymo (formerly known as the Google Self-Driving Car project). He has also collaborated closely with companies like Skydio, Google, Waymo, Boeing, as well as NASA Ames. Somil is broadly interested in developing mathematical tools and algorithms for the control and analysis of robotic and autonomous systems, with a focus on bridging learning and control-theoretic approaches for safety-critical autonomous systems. Somil has received several awards, most notably the NSF CAREER award, Eli Jury Award at UC Berkeley for his doctoral research, the outstanding graduate student instructor award at UC Berkeley, and the academic excellence award at IIT Kanpur.

Detailed Bio

I was born and grew up in India in a small town called Hanumangarh. I completed my PhD in Electrical Engineering and Computer Sciences at UC Berkeley, under the supervision of Prof. Claire Tomlin in the Hybrid Systems Laboratory. Before that, I completed my B.Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur, and an M.S. in Electrical Engineering and Computer Sciences (advisor: Prof. Claire Tomlin) from UC Berkeley in 2012 and 2014, respectively. After my PhD, I spent a year as a postdoc at Waymo, working on the safety analysis of self-driving cars. I have also spent time at University of Western Ontario as a research assistant, and with the performance verification team at Broadcom. After my masters, I worked as a Business Consultant for Applied Predictive Technologies at their San Francisco office. During my PhD, I have also worked as a research intern with the autonomy team at Skydio.

I am broadly interested in the intersection of robotics, control and dynamical system theory, and machine learning. Specifically, I explore how machine learning tools can be combined with the control theoretic frameworks to develop data-efficient, performant, and safe decision-making algorithms for physical robotic systems.

In my free time, I enjoy climbing, hiking, playing board games, solving puzzles, and exploring new restaurants. I also (used to) write on Quora to share my own experience and thoughts on machine learning, control, and robotics (my Quora profile).