INVITED SPEAKERS

Fan Shi

Talk title: Towards Reliable and Trustworthy Reinforcement Learning in Robotics

National University of Singapore

Short Bio: Fan Shi (石凡) is an Assistant Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS), where he directs the Human-Centred Robotics Lab. He holds the prestigious Presidential Young Professorship at NUS. Prior to joining NUS, he was a Postdoctoral Fellow at the ETH AI Center, collaborating with Prof. Stelian Coros and Prof. Marco Hutter. He earned his Ph.D. at the JSK Lab, University of Tokyo, under the supervision of Prof. Masayuki Inaba and Prof. Kei Okada. In 2020, he was a visiting researcher at ETH Zurich's RSL Lab with Prof. Marco Hutter. He received his bachelor's degree from Peking University, advised by Prof. Huijing Zhao, and was a visiting researcher at Microsoft Research Asia (with Prof. Katsushi Ikeuchi) and Takanishi Lab (with Prof. Atsuo Takanishi). Beyond academia, he has contributed to IEEE Spectrum Robotics with Erico Guizzo and Evan Ackerman and collaborated with his longtime friend Yifan Hou on the Chinese robotics media platform 机器人学家. His long-term goal is to "lifelong target is to build the good stuff, and help the good happen."

Alfred Chen

UC Irvine

Talk title: How Much Do We Need to Worry about Embodied AI Security Problems in Practice? A Systems Security Perspective for Embodied AI Technologies

Short Bio: Alfred Chen is an Assistant Professor of Computer Science at the University of California, Irvine. His research interest broadly lies in the security and privacy of computer technologies of high criticality to daily life and society. His current focus is on the security/privacy issues in emerging AI/systems/network technologies, especially those empowering the emerging autonomous vehicle and intelligent transportation systems. His works have high impacts in both academic and industry with 50+ research papers in top-tier venues across security, AI/ML, robotics, mobile systems, transportation, and software engineering; a nationwide USDHS US-CERT alert, multiple CVEs; 50+ news coverage by major media such as Forbes, Fortune, and BBC; and vulnerability report acknowledgments from USDOT, Apple, Microsoft, etc. Recently, his research triggered 30+ auto-driving companies and the V2X standardization workgroup to start security vulnerability investigations; some confirmed to work on fixes. He co-founded the USENIX/ISOC Symposium on Vehicle Security and Privacy (VehicleSec), and co-created DEF CON’s first auto-driving-themed hacking competition. He received various awards such as NSF CAREER Award, ProQuest Distinguished Dissertation Award, and UCI Chancellor’s Award for mentoring. Chen received Ph.D. from the University of Michigan in 2018.

Hao Su

New York University

Talk Title: Autonomous Wearable Robots for Everyone and Everywhere via Learning-in-Simulation and High-Torque Motors

Short Bio: Dr. Hao Su is an Associate Professor in the Tandon School of Engineering at New York University, and he was an associate professor at North Carolina State University and University of North Carolina Chapel Hill. He was a Research Scientist at Philips Research North America, and then a postdoctoral fellow at Harvard University. Dr. Su received National Science Foundation (NSF) CAREER Award, Switzer Distinguished Fellow by U.S. Department of Health and Human Services, Toyota Mobility Challenge Discover Award, Best Medical Robotics Paper Award (Runner-up) in IEEE International Conference on Robotics and Automation (ICRA), Best Paper Award of Dynamic Systems & Control Division, American Society of Mechanical Engineers (ASME), and Philips Innovation Transfer Award. His work was published in IEEE Transactions on Robotics, IEEE/ASME Transactions on Mechatronics, Nature, Science Robotics, Nature Machine Intelligence, Science Advances, and Nature Communications. He serves as Technical Editor of IEEE/ASME Transactions on Mechatronics, associate editor of IEEE Robotics and Automation Magazine (RAM), IEEE Robotics and Automation Letters (RAL), IEEE International Conference on Robotics and Automation (ICRA), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and serves on the Editorial Advisory Board for the International Journal of Medical Robotics and Computer-Assisted Surgery.

Angela Schoellig

Technical University of Munich

Talk Title: From Geometric to Semantic Safety in Robot Learning

Short Bio: Angela Schoellig is an Alexander von Humboldt Professor of Robotics and AI at the Technical University of Munich. She is on the Board of Directors at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and coordinates the Robotics Institute Germany. Her research combines robotics, controls, and machine learning to improve robot performance, safety, and autonomy through experience-based learning. Previously, she was an Associate Professor at the University of Toronto and a Faculty Member at the Vector Institute. She has held prestigious positions, including a Canada Research Chair and CIFAR AI Chair, and was a key investigator in the NSERC Canadian Robotics Network. Angela’s accolades include the NSERC McDonald Fellowship (2022), the RSS Early Career Spotlight Award (2019), and a Sloan Fellowship (2017). She was named an MIT Innovator Under 35 and has led her team to four wins in the SAE AutoDrive Challenge. She earned her PhD at ETH Zurich and holds master's degrees from Stuttgart and Georgia Tech.

Masha Itkina

Toyota Research Institute

Talk Title: Evaluation and Uncertainty in the Age of Robot Learning

Short Bio: 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.

Luca Carlone

MIT

Talk Title: Spatial AI for Everyday Robotics

Short Bio: Luca Carlone is the Boeing Career Development Associate Professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology, and a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). He received his PhD from the Polytechnic University of Turin in 2012. He joined LIDS as a postdoctoral associate (2015) and later as a Research Scientist (2016), after spending two years as a postdoctoral fellow at the Georgia Institute of Technology (2013-2015). His research interests include nonlinear estimation, numerical and distributed optimization, and probabilistic inference, applied to sensing, perception, and decision-making in single and multi-robot systems. His work includes seminal results on certifiably correct algorithms for localization and mapping, as well as approaches for visual-inertial navigation and distributed mapping. He is a recipient of the 2024 Outstanding Systems Paper Award at RSS 2024, the 2022 and the 2017 Transactions on Robotics King-Sun Fu Memorial Best Paper Award, the Best Student Paper Award at IROS 2021, the Best Paper Award in Robot Vision at ICRA 2020, a 2020 Honorable Mention from the IEEE Robotics and Automation Letters, a Track Best Paper award at the 2021 IEEE Aerospace Conference, the Best Paper Award at WAFR 2016, the Best Student Paper Award at the 2018 Symposium on VLSI Circuits, and he was best paper finalist at RSS 2015, RSS 2021, and WACV 2023. He is also a recipient of the AIAA Aeronautics and Astronautics Advising Award (2022), the NSF CAREER Award (2021), the RSS Early Career Award (2020), the Sloan Research Fellowship (2023), the Google Daydream Award (2019), the Amazon Research Award (2020, 2022), and the MIT AeroAstro Vickie Kerrebrock Faculty Award (2020). He is an IEEE senior member and an AIAA associate fellow. 

Sebastian Scherer

Field AI & Carnegie Mellon University

Talk Title: The Safety Case for Embodied Intelligence

Short Bio: Dr. Sebastian Scherer is the Director of Fieldable Embodied AI at Field AI and Associate Research Professor at Carnegie Mellon University's Robotics Institute. A globally-renowned expert, his groundbreaking research has redefined what is possible in deployable AI, with a focus on creating safe, adaptive systems for embodied intelligence in uncertain and dynamic environments. Dr. Scherer’s pioneering research has driven major advancements in field robotics, from autonomous landing systems to obstacle avoidance and mobility, influencing both academic and industry standards. His groundbreaking work continues to shape the future of AI by enabling scalable, high-performance systems capable of safely navigating real-world challenges.

Marco Pavone

Stanford University

Talk Title: Ensuring Physical AI Safety in AI-Enabled Autonomous Systems

Short Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he directs the Autonomous Systems Laboratory and the Center for Automotive Research at Stanford. He also leads autonomous vehicle research at NVIDIA. 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.

Soon-Jo Chung

Caltech

Talk Title: Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems

Short Bio: Soon-Jo Chung is the Bren Professor of Aerospace in the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT).  Prof. Chung is also a Research Scientist of the NASA Jet Propulsion Laboratory. Prof. Chung received the S.M. degree in Aeronautics and Astronautics and the Sc.D. degree in Estimation and Control with a minor in Optics from MIT in 2002 and 2007, respectively. He received the B.S. degree in Aerospace Engineering from KAIST in 1998.

From 2009 to 2016, Prof. Chung was an associate professor and an assistant professor at the University of Illinois at Urbana-Champaign. Prof. Chung was a Member of the Guidance & Control Analysis Group in the Jet Propulsion Laboratory as a JPL Summer Faculty Research Fellow and Faculty Affiliate working on distributed small satellites during the summers of 2010-2014.  Professor Chung's research focuses on distributed spacecraft systems, space autonomous systems, and aerospace robotics, and in particular, on the theory and application of complex nonlinear dynamics, control, estimation, guidance, and navigation of autonomous space and air vehicles. He is the recipient of the UIUC Engineering Dean's Award for Excellence in Research, the Beckman Faculty Fellowship of the U of Illinois Center for Advanced Study, the AFOSR Young Investigator Program (YIP) award, the NSF CAREER award, three best conference paper awards (2015 AIAA GNC, 2009 AIAA Infotech, 2008 IEEE EIT), and three best student paper or finalist awards. He also received multiple teaching awards including the UIUC List of Teachers Ranked as Excellent and the instructor/advisor for the 1st place national winning team of the AIAA Undergraduate Team Space Design Competition.

Prof. Chung is an Associate Editor of IEEE Transactions on Robotics, IEEE Transactions on Automatic Control, and AIAA Journal of Guidance, Control, and Dynamics. He was the lead guest editor of the Special Section on Aerial Swarm Robotics published in the IEEE Transactions on Robotics.

Sydney Katz

Stanford University

Talk Title: Building a Safety Case for AI in Aviation

Short Bio: Sydney Katz is a postdoctoral researcher in the Aeronautics and Astronautics department at Stanford University. She is advised by Professor Mykel Kochenderfer in the Stanford Intelligent Systems Laboratory (SISL). Her research is focused on the design and validation of safety-critical decision-making systems, and she is an author of the textbook Algorithms for Validation. Sydney received her Ph.D. in Aeronautics and Astronautics from Stanford University in 2023 and her M.S. in Aeronautics and Astronautics from Stanford University in 2020. Before coming to Stanford, she received her B.S. and B.S.A.S in Electrical and Systems Engineering from Washington University in St. Louis.