Chris is a graduate student in the Department of Computer Science advised jointly by Professors Jeannette Bohg and Marco Pavone. His research focuses on how complex robotic behavior can be learned from data and embodied interaction (reinforcement learning), along with how to use these behaviors to feasibly and efficiently plan for any task (task & motion planning).
Prior to joining Stanford, Chris graduated with honors from University of Toronto’s Engineering Science program. In that time, he conducted research in robot vision, mapping, planning, and control with UofT’s Robot Vision and Learning Lab, Autonomous Systems and Biomechatronics Lab, and McGill’s Mobile Robotics Lab. Chris has also held internships with Microsoft Mixed Reality, Google Cloud, and Noah’s Ark Research Labs.
Beyond research, Chris enjoys practicing soccer, tennis, going on trail runs, reading, and playing music’s golden age on the guitar, bass and drums.