Georgia Institute of Technology College of Sciences

Leveraging large-scale data and systems of computing accelerators, statistical learning has led to  significant paradigm shifts in many scientific disciplines. Grand challenges in science have been tackled with exciting synergy between disciplinary science, physics-based simulations via high-performance computing, and powerful learning methods.

In this talk, I will describe several vignettes of our research in the theme of modeling complex dynamical systems characterized by partial differential equations with turbulent solutions. I will also demonstrate how machine learning technologies, especially advances in generative AI technology,  are effectively applied to address the computational and modeling challenges in such systems, exemplified by their successful applications to  weather forecast and climate projection. I will also discuss what new challenges and opportunities have been brought into future machine learning research.

The research work presented in this talk is based on joint and interdisciplinary research work of several teams at Google Research, ETH and Caltech.


Bio: Dr. Fei Sha is currently a research scientist at Google Research, where he leads a team of scientists and engineers working on scientific machine learning with a specific application focus towards AI for Weather and Climate. He was a full professor and the Zohrab A. Kaprielian Fellow in Engineering at the Department of Computer Science, University of Southern California. His primary research interests are machine learning and its application to various AI problems: speech and language processing, computer vision, robotics and recently scientific computing, dynamical systems, weather forecast and climate modeling.  Dr. Sha was selected as a Alfred P. Sloan Research Fellow in 2013, and also won an Army Research Office Young Investigator Award in 2012. He has a Ph.D from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc from Southeast University (Nanjing, China). More information about Dr. Sha's scholastic activities can be found at his microsite at http://feisha.org.