About

I’m broadly interested in the intersection of physics and machine learning—a powerful combination for modeling complex systems, understanding learning, and advancing algorithms. In particular, I focus on the connections between reinforcement learning (RL) and statistical mechanics. What once seemed like a simple application of statistical mechanics to RL has now blossomed into a rich field of research, with many open questions and exciting connections to other fields – now I am deep down the rabbit hole 🕳️🐇 and having fun exploring the rich field of reinforcement learning.

Short Bio:

In high school, I dual-enrolled at Lorain County Community College, graduating with an A.S. before transferring to Cleveland State University, where I started academic research and completed my honors B.S. degrees in Physics and Mathematics.

In fall 2020, I began my graduate studies at the University of Massachusetts Boston, where I have served as a Teaching Assistant for undergraduate physics labs and introductory physics discussions. I am now a full-time Research Assistant supervised by Professor Rahul V. Kulkarni. As of fall 2022, I am a PhD candidate in the Department of Physics at UMass Boston. I am also a member of the Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). I am also an intern at Sony AI, where I work on average-reward deep RL algorithms, with applications to video games. You can find my CV here.

Today, I have many wide-ranging interests including reinforcement learning, statistical physics, thermodynamics, gene expression networks, superconductivity, polymer physics, graph theory, and applications of mathematics to machine learning and physics.

My current research efforts are focused on the connection between reinforcement learning and statistical mechanics. You can read more about my past and current research here.