I am a mathematics Ph.D. candidate at Colorado State University expecting to defend in early spring of 2027. I enjoy thinking about how ideas from pure mathematics can advance deep learning.

My research focuses on extending modern equivariant learning architectures to settings in which signals live on Riemannian manifolds. In particular, I’ve extended Maurice Weiler’s gauge field theory of convolutional neural nets to attention like mechanisms. I am in the process of implementing a novel equivariant sphere transformer which can process fields of arbitrary type.

In parallel, I apply machine learning and statistical methods to extract reliable insight from biological datasets in the small-n, large-p regime.