Speakers

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. 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 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. His work has been recognized with best paper nominations or awards at the European Control Conference, at the IEEE International Conference on Intelligent Transportation Systems, at the Field and Service Robotics Conference, at the Robotics: Science and Systems Conference, at the ROBOCOMM Conference, and at NASA symposia. He is currently serving as an Associate Editor for the IEEE Control Systems Magazine. He is serving or has served on the advisory board of a number of autonomous driving start-ups (both small and multi-billion dollar ones), he routinely consults for major companies and financial institutions on the topic of autonomous systems, and is a venture partner for investments in AI-enabled robots.

Yunzhu Li is an Assistant Professor of Computer Science at Columbia University.

Before joining Columbia, Yunzhu was an Assistant Professor at UIUC CS. He also spent time as a Postdoc at the Stanford Vision and Learning Lab (SVL), working with Fei-Fei Li and Jiajun Wu. He received my PhD from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, where he was advised by Antonio Torralba and Russ Tedrake, and he obtained his bachelor's degree from Peking University.

Yunzhu's group at RoboPIL work at the intersection of robotics, computer vision, and machine learning. Specifically, we focus on Robot Learning and aim to significantly expand robots' perception and physical interaction capabilities, particularly through the following three directions.

• Structured World Models: Learning physics-inspired predictive models from and for robotic manipulation of deformable objects.

• Embodied Intelligence: Developing and integrating robotic foundation models for generalizable and long-horizon embodied interactions.

• Multi-Modal Perception: Harnessing vision, touch, audio, and language for fine-grained and effective manipulation.

Subbarao Kambhampati (Rao) directs the Yochan research group which is associated with the AI Lab at ASU. In the good old days, he used to be mostly interested in planning and decision-making capabilities for autonomous agents. Lately he discovered humans, and decided they are fun to keep around as AI takes over the world. Accordingly, his current research agenda revolves mainly around human-aware AI systems.

He is the recipient of a 1992 NSF Research Initiation Award, a 1994 NSF young investigator award, a 2001-2002 College of Engineering teaching excellence award, and a 2004 IBM Faculty Award and multiple Google Research Awards (2007, 2010, 2013 & 2016). In 2004, he was named a Fellow of AAAI (Association for Advancement of Artificial Intelligence). In 2011, he was selected by ASU students to give a Last Lecture. In 2017, he was elected a fellow of the American Association for the Advancement of Science (AAAS). In 2018, he was dubbed a "distinguished almnnus" by the Computer Science Department of University of Maryland, College Park (with his own wall plaque and all!). In 2019, he was named a fellow of the Association for the Computing Machinery (ACM). In 2022, IIT Madras also relented and recognized him as a "distinguished alumnus" of the Institute!. He is also the recipient of the 2025 AAAI/EAAI Patrick H. Winston Oustanding Educator Award.

Lucy Shi is a CS PhD student at Stanford, advised by Chelsea Finn, and a researcher at Physical Intelligence (Pi). Previously, She worked with Yuke Zhu and Jim Fan at NVIDIA Research. Lucy received the Bachelor’s degree in CS from USC, advised by Joseph J. Lim and Youngwoon Lee.

Lucy is broadly interested in robot learning. Lucy's research goal is to create general-purpose robots that seamlessly perform complex, long-horizon tasks in our daily lives — from homes to factories, handling both the tedious and the dangerous. Lucy is drawn to developing simple methods that scale well with compute and data.

Luck deeply believes in human ingenuity and the potential of AI. Her goal for the next 20 years is to become a university professor, and she wants to establish the next Bell Labs — the idea factory that revolutionized the world for the better.

Gregory Stein is an Assistant Professor of Computer Science at George Mason University, where he runs the Robotic Anticipatory Intelligence & Learning (RAIL) Group. Their research, at the intersection of robotics and machine learning, is centered around developing representations that allow robots to better understand the impact of their actions, so that they may plan quickly and intelligently in a dynamic and uncertain world.
Greg Stein wants to bring about a future in which robots act more intelligently in a dynamic and uncertain world and engender trust in the humans with whom they share it. Even as robots have started to populate our world, they are limited in the types of things they can do and struggle to accomplish even simple tasks in places that they've never been before. Stein's research is devoted to changing how robots think about uncertainty so that they can make predictions about their surroundings from experience and understand the role their actions play in shaping their environment. His work so far—nominated for best paper and awarded best oral presentation at the 2018 Conference on Robot Learning—has predominantly focused on effective navigation in previously unseen buildings, and he is working on expanding his research to improve home-care robotics and self-driving vehicles. His ongoing work is devoted to imbuing autonomous agents with the ability to better explain their decision-making when faced with uncertainty, a critical capability if robots are to realize their full potential and safely operate alongside people.

Stein believes that effective communication and mentorship are critical components of a successful research lab. As a graduate student, he was an active member of the community, and his work as a volunteer communication advisor and as a wellness coach helped him to become an effective communicator and to cope with the challenges of graduate school, experiences he works to pass along to his students.

Yongchao Chen is a 4th year PhD student of Electrical Engineering at Harvard SEAS and MIT LIDS. Yongchao currently works on Neuro-Symbolic Foundation Models for Planning under the guidance of Prof. Chuchu Fan and Prof. Nicholas Roy at MIT and co-advised by Prof. Na Li at Harvard. Yongchao also do the research in AI for Physics, Mechanics, and Materials, particularly interested in applying Robotics/Foundation Models into AI4Science.

Yongchao received the bachelor's degree at University of Science and Technology of China with the major in Theoretical and Applied Mechanics and minor in Applied Mathematics in 2021. Yongchao interned at Google Research, Microsoft Research,  and work with MIT-IBM Watson AI Lab starting from 2023.