MASCLE Machine Learning Seminar: Rose Yu (Northeastern University) - Physics Guided AI for Learning Spatiotemporal Dynamics
Thu, Feb 20, 2020 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rose Yu, Northeastern University
Talk Title: Physics Guided AI for Learning Spatiotemporal Dynamics
Series: Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: Applications such as sports, climate science, and aerospace engineering require learning complex dynamics from large-scale spatiotemporal data. Such data is often non-linear, non-Euclidean, high-dimensional, and demonstrates complicated dependencies. Existing machine learning frameworks are still insufficient to learn spatiotemporal dynamics as they often fail to exploit the underlying physics principles. I will demonstrate how to inject physical knowledge in AI to deal with challenges such as non-linear dynamics, non-Euclidean geometry, and multi-resolution structure. I will showcase the application of these methods to problems such as accelerating turbulence simulations, imitating basketball gameplay and combating ground effect in quadcopter landing.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Yu is an Assistant Professor in the Khoury College of Computer Sciences at Northeastern University. Previously, she was a postdoctoral researcher at Caltech Computing and Mathematical Sciences. She earned her Ph.D. in Computer Sciences at the University of Southern California. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data, with a particular emphasis on physics-guided AI. Among her awards, she has won Google Faculty Research Award, the NSF CRII award, best dissertation award in USC, best paper award at the NeurIPS time series workshop, and was nominated as one of the \'MIT Rising Stars in EECS\'.
Host: Yan Liu
Audiences: Everyone Is Invited
Contact: Computer Science Department