INVITED SPEAKERS

Justin Carpentier

INRIA Paris

Short Bio: Justin Carpentier is a researcher at Inria Paris and team leader of the Willow research group at the Computer Science Department of École Normale Supérieure (ENS). His research focuses on perception, machine learning, optimization, and control for robotics. In 2024, he was awarded an ERC Starting Grant for his project ARTIFACT: The ARTIficial Motion FACTory, which will begin in 2025. Justin joined the Willow group in 2018 as a postdoctoral fellow after completing a PhD (2014-2017) in the Gepetto research group at LAAS-CNRS in Toulouse, followed by a postdoctoral appointment in the same group. His doctoral work explored the computational foundations of anthropomorphic locomotion, highlighting mechanisms underlying human bipedalism and developing new mathematical formulations for humanoid robot locomotion. He received his degree in Computer Science and Applied Mathematics with highest honors from École Normale Supérieure Paris-Saclay in 2013, and was a visiting student in Katja Mombaur’s Optimization in Robotics and Biomechanics group at the University of Heidelberg in 2014.

Ming Lin

University of Maryland & Amazon

Short Bio: Ming Lin is a Distinguished University Professor at the University of Maryland, College Park, with appointments in Computer Science, Electrical and Computer Engineering, the Institute for Systems Research, and Applied Mathematics & Statistics. Her research spans AI and robotics, computer vision and perception, physically based modeling, graphics and visualization, virtual and augmented reality, human-computer interaction, and scientific computing. Professor Lin has made pioneering contributions in collision detection, physics simulation, crowd animation, haptics, sound rendering, and robotics. She has been recognized as a Fellow of the ACM, IEEE, Eurographics, and NAI, and is a member of the ACM SIGGRAPH Academy and the IEEE VGTC Virtual Reality Academy. Her honors include the ACM SIGGRAPH Seminal Graphics Paper Award, IEEE VGTC Technical Achievement Award, and recognition as one of Robohub’s 50 Women in Robotics You Need to Know. She is currently contributing her expertise in AI and robotics to the MATRIX Lab’s research on autonomous technologies and uncrewed systems, while also engaging in initiatives such as the Autonomy Summit and Evening@SMART.

Jiajun Wu

Stanford University

Short Bio: Jiajun Wu is an Assistant Professor of Computer Science, and by courtesy, of Psychology at Stanford University. His research focuses on physical scene understanding—developing AI systems that can see, reason about, and interact with the physical world. His group studies the representations and levels of abstraction needed for such understanding, drawing inspiration from both the physical world and human cognition. Jiajun’s representative projects include Galileo, MarrNet, 4D Roses, the Neuro-Symbolic Concept Learner, and Scene Language. His work spans multimodal perception from visual, acoustic, and tactile signals (e.g., ObjectFolder, RealImpact), visual generation of physical 3D/4D worlds (e.g., 3D-GAN, pi-GAN, Point-Voxel Diffusion, SDEdit, WonderWorld), neuro-symbolic approaches to visual reasoning (e.g., NS-VQA, Shape Programs, CLEVRER, LEFT), and applications in robotics and embodied AI (e.g., RoboCook, BEHAVIOR). Before joining Stanford, he was a Visiting Faculty Researcher at Google Research. He received his PhD from MIT, advised by Bill Freeman and Josh Tenenbaum, and his undergraduate degree from Tsinghua University, where he worked with Zhuowen Tu.

Angela Schoellig

Technical University of Munich

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.

Jason Peng

Simon Fraser University & NVIDIA

Short Bio: Jason Peng is an Assistant Professor at Simon Fraser University and a Research Scientist at NVIDIA. His research lies at the intersection of computer graphics and machine learning, with a focus on reinforcement learning for motion control of simulated characters. His work develops methods that enable virtual agents to acquire complex, naturalistic behaviors, bridging advances in machine learning with applications in graphics, animation, and robotics. He received his PhD from the University of California, Berkeley, advised by Sergey Levine and Pieter Abbeel, and his MSc from the University of British Columbia, advised by Michiel van de Panne. He has also worked with leading research groups in both academia and industry, including Sony, Google Brain, OpenAI, Adobe Research, Disney Research, Microsoft (343 Industries), and Capcom.

Maurizio Chiaramonte

Sergey Zakharov

Toyota Research Institute

Short Bio: Sergey Zakharov is a Senior Research Scientist and Team Lead at the Toyota Research Institute. His research focuses on 3D scene understanding for robust and versatile perception models, enabling machines to infer rich representations of their environment from vision. His work spans generative modeling, implicit neural representations, 3D reconstruction, domain adaptation, and self-supervised learning, with a particular emphasis on generalization and real-world transfer. He received his PhD from the Technical University of Munich, where he was advised by Slobodan Ilic.

Hugo Talbot

INRIA Nancy

Short Bio: Hugo Talbot is the Coordinator of the SOFA Consortium at Inria, where he has led the development and community growth of the open-source SOFA project since 2016. His mission includes supporting research and industrial projects built on SOFA, defining the technical roadmap in collaboration with consortium members, and fostering technology transfer and community engagement. He received a double degree in mechanical engineering from the Karlsruhe Institute of Technology and INSA Lyon in 2010, and earned his PhD in medical simulation from Inria in 2014, focusing on real-time simulation of the electrical activity of the human heart. He subsequently worked as a research engineer in the Mimesis team at Inria, contributing to cryoablation and cardiac electrophysiology simulation. Today, he continues to drive the SOFA Consortium’s efforts to advance open-source simulation for research, healthcare, and industry.

Mansur Arief

Stanford Center for AI Safety

Short Bio: Mansur Arief is the Executive Director of the Stanford Center for AI Safety and a Research Engineer with the Stanford Intelligent Systems Lab (SISL) and Mineral-X. His research integrates machine learning, optimization, and simulation to build trustworthy AI systems for safety-critical and sustainability applications. He focuses on resilient critical mineral supply chains, safety-aware geothermal and subsurface systems, and robust verification and validation methods for AI in high-stakes domains. He earned his PhD in Mechanical Engineering from Carnegie Mellon University, advised by Ding Zhao in the Safe AI Lab, and also serves as an Adjunct Professor at ULBI in Indonesia.

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