일시: 2026. 7. 10. (금요일), 오후 1시 - 2시 30분

장소: 제4공학관 D503호

Speaker: Ming C. Lin, Ph.D. / Distinguished University Professor,  University of Maryland at College Park & Amazon FAR

Title: Dynamics-Informed Learning in the Physical World

Abstract:

Recent progress in data-driven revolution has led to significant advances in artificial intelligence (AI), while many challenges remain in terms of computational efficiency, AI safety, and generalization.  In this talk, we present latest advances to address some of these issues. First, we introduce efficient Time-Aware World Model (TAWM) that minimizes training costs with improved accuracy by adaptively sampling multiple time steps across scale for greener computing and energy efficiency for generalist robots and visual odometry of mobile robots.   Next, we introduce the first end-to-end framework that integrates linear-temporal logic with differentiable simulation, enabling efficient gradient-based learning directly from formal specification. We also present an immersive vehicle-traffic coupled simulation system that models diverse sets of adversarial scenarios, including accidents, extreme weather conditions, human behaviors, and unexpected events, to sample and capture driving data in tail-end distribution.  Lastly, we discuss techniques to construct a generalizable 3D foundation model of garments for virtual try-on.  Together these methods showcase a collection of data generation and sampling strategies that can provide more computational efficient, representative, and reliable data synthesis for robust and generalizable learning. They offer new insights for addressing some data challenges for more robust and effective learning from simulation and observations.  I conclude by discussing some possible future directions and data challenges for learning in the Physical World.

Bio:

Ming C. Lin is currently Distinguished University Professor, Dr. Barry Mersky and Capital One E-Nnovate Endowed Professor, former Elizabeth Stevinson Iribe Chair of Computer Science at the University of Maryland College Park, and John R. & Louise S. Parker Distinguished Professor Emerita of Computer Science at the University of North Carolina (UNC), Chapel Hill.  She is also an Amazon Scholar.  She obtained her B.S., M.S., and Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley. She received several honors and awards, including the NSF Young Faculty Career Award in 1995, Honda Research Initiation Award in 1997, UNC/IBM Junior Faculty Development Award in 1999, UNC Hettleman Award for Scholarly Achievements in 2003, Beverly W. Long Distinguished Professorship 2007-2010, UNC WOWS Scholar 2009-2011, IEEE VGTC Virtual Reality Technical Achievement Award in 2010, Washington Academy of Science Distinguished Career Award 2020, ACM SIGGRAPH Seminal Graphics Paper in Physical Simulation, IEEE ICRA 2026 Most Influential Paper Award, and many best paper awards.  She is a Fellow of National Academy of Inventors, ACM, IEEE, Eurographics, ACM SIGGRAPH Academy, and IEEE VR Academy.