I am a Director of Data Science at Capital One working on projects for large-scale customer graphs for entity resolution, GenAI methods and I'm very passionate about building models that actually make it into production.
My background is in computational physics, which is how I originally got into machine learning. These days I’m interested in foundation models for their in-context capabilities (e.g. tabPFN), interpretable architectures for decision making, and ways to use scalable modern ML methods.
I lead a fantastic team of data scientists who work across business areas like fraud, servicing, and marketing, and a lot of my daily work involves taking good research ideas and figuring out how to make them useful inside a real enterprise system, which usually entails a lot governance and risk management.
On this site I keep notes, side projects, and ideas I’m exploring. Most of this isn’t polished, and that’s intentional—it’s a place for experiments, learning, and ongoing work. If you find anything useful here (or want to chat about ML,science & software engineering), feel free to reach out.
I previously received my Ph.D. from Boston University doing research with Pankaj Mehta. You can check my prior recent and older publications and some older ML repos I built.