Gavin Kerrigan

Research

I work broadly in probabilistic machine learning, with a focus on generative models, optimal transport, and experimental design. 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, 9].

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.

Working together

If you are a current Oxford postgraduate student interested in doing a miniproject, or are otherwise interested in collaborating with me, please do feel free to reach out.

Previously

I was previously a postdoctoral research assistant in the RainML lab led by Tom Rainforth. 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 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.