Office: Searle 202

I am a Schmidt AI in Science Fellow at the University of Chicago working at the intersection of physics and machine learning. My research interests include physics-informed machine learning and interpretable representational learning with applications in nonlinear dynamics, condensed matter physics, photonics, fluid dynamics, biophysics, and other areas. I aim to develop new computational methods for modeling and understanding physical systems with an emphasis on incorporating physics-informed priors and identifying relevant and interpretable latent representations.

I received a Ph.D. in Physics from MIT in 2022, and an A.B. in Physics and Mathematics from Harvard in 2016.

## News

Feb 16, 2022 APS March Meeting 2022

## Selected Publications

1. Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Transactions of Machine Learning Research (2022)
2. Discovering Conservation Laws using Optimal Transport and Manifold Learning
(2022)
3. Discovering sparse interpretable dynamics from partial observations
Communications Physics (2022)
4. Discovering Dynamical Parameters by Interpreting Echo State Networks
NeurIPS 2021 AI for Science Workshop (2021) — Best Paper Award
5. Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
IEEE Transactions on Neural Networks and Learning Systems (2021)
6. Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
Physical Review X (2020)
7. Energy Loss at Propagating Jamming Fronts in Granular Gas Clusters
Physical Review Letters (2013)