Short Bio: Animesh Garg is the Stephen Fleming Early Career Professor in Computer Science at Georgia Tech, within the School of Interactive Computing, and is affiliated with the Robotics and Machine Learning programs. He holds courtesy appointments at the University of Toronto and the Vector Institute. Previously, he held research leadership positions at Nvidia and Apptronik. His research focuses on the algorithmic foundations of generalizable autonomy, enabling robots to acquire cognitive and dexterous skills and collaborate with humans in novel environments. His group explores structured inductive biases, causality in decision-making, multimodal object-centric representations, self-supervised learning for control, and efficient dexterous skill acquisition.

Short Bio: Daniel Ho is the Director of Evaluation at 1X Technologies. His goal is to deploy generalist machines that grow from experience and correct their own mistakes. He's building World Models and large-scale evaluation pipelines towards this mission. Previously, he worked on robotics, perception, and machine learning as a Senior Software Engineer at Waymo and Everyday Robots (X, Google[X]). His research has focused on learning algorithms and representation learning to generalize ML model understanding in robotics, computer vision, and self-driving.

Talk Title: 1X World Model: Solving humanoid policy training and evaluation with data synthesis and action control

Short Bio: Hao Su is an Associate Professor of Computer Science at UC San Diego and Founder & CTO of Hillbot, a robotics startup. He directs the Embodied Intelligence Lab and is a founding member of the Halıcıoğlu Data Science Institute. His research spans computer vision, machine learning, graphics, and robotics, focusing on algorithms to simulate and interact with the physical world. He holds Ph.D.s in Computer Science from Stanford and Mathematics from Beihang University. He helped develop datasets like ImageNet, ShapeNet, and tools like PointNet. Su is Program Chair of CVPR 2025 and has received NSF CAREER and SIGGRAPH awards.

Talk Title:Learning World Models for Embodied AI

Short Bio: Katerina Fragkiadaki is the JPMorgan Chase Associate Professor in Machine Learning at Carnegie Mellon University. She earned her B.S. from the National Technical University of Athens and her Ph.D. from the University of Pennsylvania, followed by postdoctoral work at UC Berkeley and Google Research. Her research combines common sense reasoning with deep visuomotor learning to enable few-shot and continual learning for perception, action, and language grounding. Her group develops methods in 2D-to-3D perception, vision-language grounding, and navigation policies. She received awards including the NSF CAREER and DARPA Young Investigator Awards and is Program Chair for ICLR 2024.

Talk Title: From Videos to Physics Engine Simulations to Neural Simulations

Short Bio: Yilun Du is an Assistant Professor at Harvard’s Kempner Institute and Computer Science Department, and a Senior Research Scientist at Google DeepMind. He earned his Ph.D. in EECS from MIT, advised by Leslie Kaelbling, Tomas Lozano-Perez, and Joshua Tenenbaum. He holds a bachelor’s from MIT and has been a research fellow at OpenAI and a visiting researcher at FAIR and DeepMind. A gold medalist at the International Biology Olympiad, his research focuses on generative models, decision making, robot learning, and embodied agents. He develops energy-based models enabling generalization and advances in diffusion models, scene understanding, and trajectory planning.

Talk Title: Learning Generative World Simulators

Short Bio: Dr. Russ Tedrake is Senior Vice President of Large Behavior Models at Toyota Research Institute (TRI) and Toyota Professor at the Massachusetts Institute of Technology (MIT). The Large Behavior Models division is building foundation models for dexterous robotic manipulation and includes world-class researchers who focus on machine learning (especially computer vision and large multimodal models), dynamics and simulation, human-robot interaction, and hardware and software for dexterous manipulation.

Talk Title: TBD

Short Bio: Boyi Li is a Research Scientist in the Autonomous Vehicle Research group at NVIDIA. She is broadly interested in the computer Vision, machine Learning and multimedia Art. Particularly, her research focuses on multimodal and data-efficient machine learning. Her research vision is to enable interactive, user-friendly and reliable autonomy for a broad range of high-integrity robotics applications. Prior to joining NVIDIA, received her Ph.D. at Cornell University, advised by Prof. Serge Belongie and Prof. Kilian Q. Weinberger.

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