Organizers

Rowan McAllister, Berkeley, rmcallister@berkeley.edu [www]

Rown McAllister is a postdoctoral researcher at UC Berkeley working on motion planning for autonomous vehicles at the Berkeley Deep Drive group and Robotic AI & Learning Lab, supervised by Sergey Levine. He previously worked with Uber's autonomous vehicle forecasting team, completed his PhD at the University of Cambridge on Bayesian reinforcement learning, and a masters in motion planning at the Australian Center for Field Robotics. Some of Rowan’s recent works include goal-conditioned multi-agent forecasting for autonomous driving. Rowan was the primary organizer of the NeurIPS 2019 workshop on Machine Learning for Autonomous Driving, and an organizer of the ICLR 2019 workshop on Task-Agnostic Reinforcement Learning.

Litin Sun, Berkeley, litingsun@berkeley.edu

Liting Sun is a Postdoctoral researcher at UC Berkeley working on human robot interaction with applications to autonomous driving and intelligent robots, supervised by Professor Masayoshi Tomizuka. She obtained her Ph.D. in 2019 at UC Berkeley. Her research focuses on intelligent and high-performance behavior design for interactive autonomous systems, merging ideas from optimization, control, behavior economics, and machine learning. She is also a key member compiling the Interaction Dataset. She has organized and co-organized multiple workshops on datasets and behavior design for autonomous vehicles on conferences such as IEEE Intelligent Vehicles Symposium and International Conference on Intelligent Robots and Systems.

Igor Gilitschenski, MIT, igilitschenski@mit.edu [www]

Igor Gilitschenski is a Senior Postdoctoral Associate within the Computer Science and Artificial Intelligence Lab at MIT. Supervised by Daniela Rus and Sertac Karaman, he is working on perception for autonomous driving. Prior to that, he was affiliated with the Autonomous Systems Lab of ETH Zurich working with Roland Siegwart on robotic perception, particularly localization and mapping. He obtained a PhD degree in computer science working on nonlinear estimation at the Karlsruhe Institute of Technology supervised by Uwe Hanebeck and Simon Julier. His work has received best paper awards at the American Control Conference and the International Conference on Information Fusion. His research interest involves developing novel learning and inference techniques for perception and control of autonomous systems.

Daniela Rus, MIT, rus@csail.mit.edu [www]

Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’ research interests are in robotics, artificial intelligence, and data science. The focus of her work is developing the science and engineering of autonomy, toward the long-term objective of enabling a future with machines pervasively integrated into the fabric of life, supporting people with cognitive and physical tasks. Rus serves as the Associate Director of MIT’s Quest for Intelligence Core, and as Director of the Toyota-CSAIL Joint Research Center, whose focus is the advancement of AI research and its applications to intelligent vehicles. She is a member of the Toyota Research Institute advisory board. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences. She is the recipient of the 2017 Engelberger Robotics Award from the Robotics Industries Association. She earned her PhD in Computer Science from Cornell University.