About Me

I am a Director of Data Science 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, MCTS) and unsupervised learning (clustering, auto-encoders, embeddings) to problems of quantum statistical physics and applied computational biology for cancer immunotherapy. 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.

Recent projects

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.