Bio

I am a theoretical physicist by training, and my current research sits at the intersection of deep learning, statistical mechanics, and mathematical physics.

I completed my PhD in physics at UC Berkeley in 2025, where I developed new approaches to dark matter detection from a condensed matter perspective. Before that, I earned an MS in applied physics from UC Berkeley and a BS in electrical engineering from Texas A&M, where I studied classical integrability.

My previous research has spanned condensed matter physics, dark matter phenomenology, nonlinear optics and dynamics, computational materials science, and quantum algorithms. See my Google Scholar for more details.

On a personal level, I enjoy building mechanical keyboards and reading. I live with my wife, dog, and two cats in the SF Bay Area.

Current Research Interests

A few questions I think about:

• Mechanistic interpretability: how do neural networks internally represent and process information?
• Physically interpretable models: can we build generative models for scientific applications, particularly amorphous materials and protein and RNA folding, that are truly rooted in physics?
• Thermodynamic computing: how do we design and train stochastic neural networks? I am interested in both hardware design and fundamental theory.

Recent blog posts

Coming soon!