I am a mathematician and machine learning researcher.

My recent research focuses on equivariant neural networks, model compression, parameter space symmetries, and gradient flow dynamics. Previously, I did research in theoretical mathematics, specifically, geometric representation theory and related areas.

This site contains pages about my math publications and writings, my work in machine learning, and a few quant finance side projects.

A copy of my CV is available upon request by email. You can also find me on GitHub.

Make the obvious replacements: ` first-name dot last-name at gmail dot com`

Until recently, I was part of the Data Science Group at Radboud University. Previously, I worked in the research group of Rami Aizenbud at the Weizmann Institute of Science, and before that in the Hausel group at IST Austria. I completed a PhD in mathematics at the University of Texas at Austin under the guidance of David Ben-Zvi. My dissertation focused on topics at the intersection of representation theory, algebraic geometry, and quantum algebra.

Fall 2022: Deep Learning.

Winter 2020-2021: Algebraic Groups.