About Me

I am a data scientist and senior manager at Capital One. I received my Ph.D. from Boston University doing research with Pankaj Mehta. My thesis was focused on applying machine learning methods such as reinforcement learning (Q-learning) and unsupervised learning (clustering and auto-encoders) to problems of quantum statistical physics and big data biology. If you're interested in learning more about applications of machine learning to physical sciences or just curious to learn more about machine learning, check out our introductory review to ML supplemented with many useful Python Jupyter notebooks. I'm always open to an impromptu chat about ML, physics, and science in general, so don't hesitate to shoot me an email if you have questions! In my spare time I enjoy developing Python packages for machine learning and finding ways to make everything O(N log(N)).

Recent projects

(Oct 2022) Self-consistent scalable clustering

A self-consistent clustering approach that learns hierarchical semantics of clusters.

Machine learning review

An introduction to machine learning in the language of physicists. Covers many of the core ideas of ML.

Reinforcement learning quantum control

Using Q-learning, we studied the rich phase diagram of quantum state preparation and it's implications.