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Tutorial: Safe Learning for Decision Making
Angela Schoellig · SiQi Zhou · Lukas Brunke · Animesh Garg · Melissa Greeff · Somil Bansal

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in Deployable Decision Making in Embodied Systems (DDM) »
Event URL: http://saferobotlearning.org/ »

Author Information

Angela Schoellig (University of Toronto, Vector Institute)
SiQi Zhou (University of Toronto)
Lukas Brunke (University of Toronto)
Animesh Garg (University of Toronto, Nvidia, Vector Institute)

I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.

Melissa Greeff (University of Toronto)
Somil Bansal (University of Southern California)

Somil Bansal is an Assistant Professor at the Department of Electrical Engineering of 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 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 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.

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