Data Science Institute

Yair Shenfeld works in high-dimensional probability and its interactions with analysis, geometry, and mathematical physics. His current research interests include optimal transport, stochastic analysis, convex geometry, and renormalization group methods.

Prior to coming to Brown, Yair was a C.L.E. Moore instructor and an NSF postdoctoral fellow in the Mathematics department at MIT. He completed his PhD in Operations Research and Financial Engineering at Princeton University, and received his B.Sc. in Mathematics from MIT. 

How does your research, teaching, or other work relate to data or computational science?

My current research is in high-dimensional probability and optimal transport. Both fields are highly relevant in data science, and personally I am broadening my research into such applications. In the spring of 2025 I am scheduled to teach a graduate topics course where I will review the theory and applications of modeling probability distributions in high dimensions. Examples from recent techniques of generative modelings (normalizing flows, diffusions, etc.) aided by neural networks will be reviewed.