Marco Pavone is an associate professor at Stanford and the Director of Autonomous Vehicle Research at NVIDIA. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is currently on partial leave from Stanford University, where he is an Associate Professor of Aeronautics and Astronautics. At Stanford, he is also the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40. He is currently serving as an Associate Editor for the IEEE Control Systems Magazine. 

Mayee Chen is a PhD candidate in Computer Science at Stanford University, advised by Professor Christopher Ré. Her research focuses on advancing the fundamentals of artificial intelligence through data-centric approaches, particularly in training data curation, where she has developed techniques for data mixing, curriculum learning, and weak supervision. Her work has been recognized with a best student paper runner-up award at UAI 2022, a best paper award at an AAAI 2022 workshop, and spotlights at ICLR and NeurIPS 2023. Mayee is currently a research intern at the Allen Institute for AI (AI2), driving the data mixing efforts for OLMo 3, their next open-source large language model. She has also interned at Microsoft Research and obtained her summa cum laude B.S.E. in Operations Research and Financial Engineering from Princeton University.

Joseph Lim is an Associate Professor in the Kim Jaechul School of Artificial Intelligence at Korea Advanced Institute of Science and Technology (KAIST). Previously, he was was an assistant professor at the University of Southern California (USC). Before that, he completed his PhD at Massachusetts Institute of Technology under the guidance of Professor Antonio Torralba, and also had a half-year long postdoc under Professor William Freeman and a year long postdoc under Professor Fei-Fei Li at Stanford University. He received his bachelor degree at the University of California - Berkeley, where he worked in the Computer Vision lab under the guidance of Professor Jitendra Malik. He has also spent time at Microsoft Research, Adobe Creative Technologies Lab, and Google.

Professor Ken Goldberg is President of the Robot Learning Foundation and Chair of the Berkeley AI Research (BAIR) Lab Steering Committee. He is co-founder of Ambi Robotics and Jacobi Robotics and William S. Floyd Distinguished Chair of Engineering at UC Berkeley, where he leads research in robotics and automation including grasping, manipulation, and learning for applications in industry, homes, agriculture, and robot-assisted surgery.

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University, the William George and Ida Mary Hoover Faculty Fellow, and a co-founder of Physical Intelligence (Pi). 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 joining Stanford, she received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley.

Max Simchowitz is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University. Previously, he was a Simons-Berkeley Research Fellow, following a postdoctoral position in the Robot Locomotion Group at MIT, where he worked with Professor Russ Tedrake. He completed his PhD in Electrical Engineering and Computer Sciences at the University of California, Berkeley, advised by Professors Ben Recht and Michael Jordan. He began his academic career as a mathematics major at Princeton University, where he conducted research with Sanjeev Arora and David Blei.

Max’s research spans both the theoretical and practical aspects of machine learning, with a focus on sequential decision making, reinforcement learning, and the control of dynamical systems. He is particularly interested in how large-scale, generative AI models can transform robot learning, video prediction, and world modeling.

Masha Itkina is a Research Lead and Manager in the Large Behavior Model (LBM) division at the Toyota Research Institute (TRI). At TRI, she co-leads the Trustworthy Learning under Uncertainty (TLU) effort in the context of robotic manipulation. Her research focuses on policy evaluation, failure detection and mitigation, and active learning. Previously, she completed her PhD at the Stanford Intelligent Systems Lab (SISL) on uncertainty-aware perception for self-driving cars. Her work has been published in top-tier robotics and machine learning conferences, including RSS, CoRL, ICRA, IROS, and NeurIPS.