Out-of-Distribution Generalization in Robotics
Towards Reliable Learning-Based Autonomy

Drago joined Waymo in 2018 to lead the Research team, which focuses on pushing the state of the art in autonomous driving using machine learning, spanning multiple areas including perception, prediction, planning and simulation. Earlier in his career he spent eight years at Google; first working on 3D vision and pose estimation for StreetView, and later leading a research team which developed computer vision systems for annotating Google Photos. The team also invented popular methods such as the Inception neural network architecture, and the SSD detector, which helped win the Imagenet 2014 Classification and Detection challenges. Prior to joining Waymo, Drago led the 3D Perception team at Zoox.

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University, and the William George and Ida Mary Hoover Faculty Fellow. Her research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has pioneered end-to-end deep learning methods for vision-based robotic manipulation, meta-learning algorithms for few-shot learning, and approaches for scaling robot learning to broad datasets. Her research has been recognized by awards such as the Sloan Fellowship, the IEEE RAS Early Academic Career Award, and the ACM doctoral dissertation award, and has been covered by various media outlets including the New York Times, Wired, and Bloomberg. Prior to Stanford, she received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley.

Amy Zhang is an assistant professor at UT Austin in the Chandra Family Department of Electrical and Computer Engineering. Her group, Machine Intelligence through Decision-making and Interaction (MIDI) Lab, focuses on theory and algorithms for sequential decision-making problems, with an emphasis on reinforcement learning, self-supervised learning, and representation learning, with a focus on improving robustness, generalization, and sample efficiency. She did her PhD at McGill University and Mila - Quebec AI Institute, co-supervised by Joelle Pineau and Doina Precup. She also has an M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT. She is recruiting PhD students. 

Dr. Judy Hoffman is an Assistant Professor in the School of Interactive Computing at Georgia Tech, a member of the Machine Learning Center, and a Diversity and Inclusion Fellow. Her research lies at the intersection of computer vision and machine learning with specialization in domain adaptation, transfer learning, adversarial robustness, and algorithmic fairness. She has received numerous awards including NSF CAREER, Google Research Scholar Award (2022), Samsung AI Researcher of the Year Award (2021), NVIDIA female leader in computer vision award (2020), AIMiner top 100 most influential scholars in Machine Learning (2020), MIT EECS Rising Star in 2015, and the NSF Graduate Fellowship. In addition to her research, she co-founded and continues to advise for Women in Computer Vision, an organization which provides mentorship and travel support for early-career women in the computer vision community. Prior to joining Georgia Tech, she was a Research Scientist at Facebook AI Research. She received her PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016 after which she completed Postdocs at Stanford University (2017) and UC Berkeley (2018). 

I work on machine learning methods for autonomous vehicle planning at Waymo in California. I was fortunate to learn from Carl Rasmussen as a PhD student, and from Sergey Levine as a postdoc, focusing on probabilistic modeling for reinforcement learning. I have also enjoyed learning from Irena Koprinska, Robert Fitch, Thierry Peynot, Jeff Schneider, and Adrien Gaidon. I am passionate about autonomous vehicles, previously working at the Australian Centre for Field Robotics and UberATG (bought by Aurora), co-founded Light Blue Labs (now Wayve), managed a research team at Toyota Research Institute on prediction and planning, and organize workshops on autonomous driving.