I am pursuing a Ph.D. in Applied Mathematics at the University of Southern California (USC). My research focuses on Wasserstein Regression, under the guidance of Dr. Xiaohui Chen. In addition to my doctoral studies, I recently completed an M.S. in Statistics at USC, and I hold an M.S. in Applied Mathematics from California State University, Northridge (CSUN) and a B.S. in Applied Mathematics from UCLA.
My academic journey has been marked by involvement in various high-impact research projects. I have had the opportunity to develop hazard simulation methods at NASA Ames Research Center and create efficient numerical methods for exterior scattering problems at the Interdisciplinary Research Institute for Sciences (IRIS). My work often integrates advanced computational tools such as Python, Fortran90, and high-performance computing, enabling me to tackle complex, multi-dimensional problems effectively.
Teaching is another passion of mine. I have taught a wide range of courses at USC and CSUN, from Precalculus to Numerical Methods. In addition, I served as President of the Math Graduate Student Association and as an advisor for the Directed Reading Program at USC, roles that allow me to support and mentor fellow students.
I have been fortunate to receive several honors and awards, including stipends from the USRA and the CSU-UCLA Math Summer Bridge Program, as well as scholarships from IRIS and Mini-Med.
In my free time, I enjoy lifting, cooking, reading & writing.
I will be on the job market Summer 2026.
NEWS
Jan 05, 2026: Neural Local Wasserstein Regression presented at the AMS Joint Mathematics Meetings in the Contributed Paper Session on Statistics.
Dec 01, 2025: Neural Local Wasserstein Regression poster presented at TAG-DS.
Nov 25, 2025: Neural Local Wasserstein Regression presented at the USC Math Graduate Colloquium.
Nov 23, 2025: Featured in the USC Math Department newsletter.
Nov 13, 2025: Neural Local Wasserstein Regression paper released on arXiv and accepted to TAG-DS 2025, introducing a nonparametric, geometry-aware framework for regression between probability distributions.