Ruiyi Yang

Ruiyi Yang is a postdoctoral researcher at Princeton University's Program in Applied and Computational Mathematics (PACM) under Prof. Amit Singer. She earned her PhD in Computational and Applied Mathematics at the University of Chicago in 2022, advised by Prof. Daniel Sanz-Alonso, and a B.S. in Mathematics from UCLA in 2017. Her research bridges data science and mathematics, focusing on Gaussian processes, inverse problems, and manifold learning.

Her work emphasizes Gaussian process methodologies for inverse problems and nonparametric statistics, with recent extensions to cryo-EM applications. She has published extensively on graph-based Bayesian semi-supervised learning, SPDE approaches to Gaussian fields, and manifold optimization algorithms.

Yang's publications highlight interdisciplinary research at the intersection of statistics, machine learning, and computational mathematics. She has taught courses in numerical analysis and applied mathematics at Princeton and the University of Chicago, with expertise in Monte Carlo methods and functional analysis.