Ruiyi Yang

Welcome to my homepage!

I am a postdoc in PACM at Princeton University under supervision of Prof. Amit Singer .

Previously, I obtained my Ph.D. 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. My research interests lie broadly in the mathematics of data science, with a particular focus on inverse problems, Gaussian process computation, and nonparametric statistics. The overall theme of my research is to design efficient algorithms that have solid mathematical foundations. Specifically, I have been working at the intersection of manifold learning and Gaussian processes on better modeling and computational tools for applications in statistical inverse problems. I also work with Prof. Bryon Aragam on nonparametric mixture models.