I am a mathematician and data scientist based in the Bay Area. My main areas of research are topology and machine learning.

I completed my Ph.D. in mathematics at Princeton University in 2016 advised by Zoltán Szabó. Since then, I’ve held academic positions at Columbia University, Stanford University, and Dartmouth College. I have also spent time in industry, as a Quantitative Researcher at Susquehanna International Group (SIG) on the equity options trading desk and at Microsoft Research in their machine learning group. I’m currently a research lead at Theorem on the trading team.

My research interests are fairly broad, but my main contributions have been in knot theory and in machine learning. In knot theory, I constructed a spectral sequence from Khovanov homology to knot Floer homology, giving a relationship between quantum and symplectic constructions and proving a conjecture that had been open since 2005. In machine learning, I helped build a framework for efficiently applying neural networks to encrypted data. An exhaustive list of publications on Google Scholar.