Abstract: The explosion of spatiotemporal data in the physical world requires new deep learning tools to model complex dynamical systems. On the other hand, dynamical system theory plays a key role in understanding the emerging behavior of deep neural networks. In this talk, I will give an overview of our research to explore the interplay between the two. I will showcase the applications of these approaches in fluid mechanics, autonomous driving, and optimization.

About the Speaker:

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 Army ECASE Award, NSF CAREER Award, Hellman Fellow, 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’.