About Me

I’m currently an Assistant Computational Scientist working in the data science group at the Leadership Computing Facility at Argonne National Laboratory.

I’m generally interested in the application of machine learning to computational problems in physics, particularly within the context of high performance computing. My current research focuses on using deep generative modeling to help build better sampling algorithms in lattice gauge theory. In particular, I’m interested in building gauge equivariant neural network architectures and using inductive priors to incorporate physical symmetries into machine learning models.

I received my PhD in Physics from the University of Iowa in 2019 and my thesis was on Learning Better Physics: A Machine Learning Approach to Lattice Gauge Theory. Prior to this, I completed two bachelors degrees (Engineering Physics and Applied Mathematics, 2015) from The University of Illinois at Urbana-Champaign. My undergraduate dissertation was titled Energy Storage in Quantum Resonators and was supervised by Professor Alfred Hübler within the Center for Complex Systems Research at UIUC.

Recent Work

Invited Talks

  • You can get a live view of some of my recent talks here