I'm a postdoctoral research assistant working on machine learning in the Department of Statistics at the University of Oxford, where I am fortunate to be a part of the RainML lab led by Tom Rainforth.
[Research Interests and Highlights]
I work broadly in probabilistic machine learning, with a focus on the role of distribution geometry in how we manipulate, optimize, and compare probability distributions. Much of my research uses tools from optimal transport to define and exploit this geometric structure. I have developed these ideas to build generative models for function-valued data [1, 2, 3] and point processes [4], to solve inverse problems arising from PDEs [5, 6], and develop new approaches for adaptive data acquisition [7, 8].
My work has been recognized with several awards, including a Best Paper Award (AISTATS 2026), a Best Student Paper Award (AISTATS 2024), and a Best Dissertation Award for my PhD thesis.
[Previously]
I completed my PhD in computer science at the University of California, Irvine, where I was advised by Padhraic Smyth. My work was supported by an HPI Fellowship, and I previously served as a workflow chair for AISTATS. While at UCI, I served as the instructor for an undergraduate machine learning course. I also hold a BSc in Mathematics from the Pennsylvania State University Schreyer Honors College.
