Scientific applications such as climate science and public health generate massive data with rich spatiotemporal information. Existing deep learning models are still limited in handling complex spatiotemporal information. We will explain how to design deep learning models to learn from large-scale spatiotemporal data, especially for dealing with physical constraints, high-dimensional uncertainty, and complex spatiotemporal dependencies. We will showcase the application of these models to problems such as turbulence prediction, climate emulation, and generating what-if scenarios for epidemic prevention and control.
Dr. Rose Yu is an associate 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. She is a recipient of the Presidential Early Career Award (PECASE) -- the highest honor given by the White House to early career scientists, 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’. She was named as MIT Technology Review Innovators Under 35 in AI.