Applications in climate science, epidemiology, and transportation often require learning complex dynamics from large-scale spatiotemporal data. While deep learning has shown tremendous success in these domains, it remains a grand challenge to incorporate physical principles in a systematic manner into the design and training of such models. In this talk, I will demonstrate how to principally incorporate physics in AI models to improve sample complexity, prediction accuracy, and physical consistency. I will showcase the applications of these models to challenging problems such as turbulence forecasting, jet tagging in particle physics, and trajectory prediction for autonomous vehicles.
Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first principles with data-driven models. Among her awards, she has won NSF CAREER Award, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the ’MIT Rising Stars in EECS’.
Ryan Adams and Peter Ramadge
Lunch available at 12:00 p.m. Please RSVP to [email protected]