I am a mathematician, machine learning researcher, data scientist, and educator. 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
My recent academic research has focused on equivariant neural networks, model compression, parameter space symmetries, and gradient flow dynamics. Previously, I did research geometric representation theory and related areas in theoretical mathematics.
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. Subsequently, I worked at IST Austria, the Weizmann Institute of Science, and the Data Science Group at Radboud University.
Fall 2022: Deep Learning.
Winter 2020-2021: Algebraic Groups.