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 Gaussian process methodologies,
inverse problems, 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 Gaussian processes and manifold learning on better modeling
and computational tools for applications in statistical inverse problems.
In another line of work, I study nonparametric mixture models on methodological development by drawing
novel theoretical insights.
Starting from my postdoc, I have also worked on mathematical problems arising from cryo-EM.
I am on the 2024-2025 academic job market.
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