Brief background

I am currently a research scientist at Google DeepMind, focusing on using state-of-the-art AI techniques to solve complex scientific problems. Before joining DeepMind, I am an instructor in the Courant Institute of Mathematical Sciences at New York University, where I combined fluid mechanics and machine learning to resolve fundamental challenges in geophysics and mathematical physics, following my previous postdoctoral work at Princeton University.

My research interests lie in the interface of fluid mechanics, applied mathematics and machine learning to address fundamental challenges in applied science and mathematical physics. I specialize in developing new methods and theories to elucidate the complex fluid phenomena governing these fields, with applications ranging from environmental transport, geophysical flows to singularity formation in diverse fluid systems. My vision is to leverage this interdisciplinary expertise to provide new predictive frameworks for critical natural and engineered systems. More research details are given on my research page.

I completed my Ph.D. in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT). Prior to MIT, I obtained a master’s degree with Distinction from the University of Cambridge, majoring in applied mathematics, and a dual bachelor’s degree in Mechanical Engineering from the University of Hong Kong and Shanghai Jiaotong University.