Office: Searle 202
I am a Postdoctoral Scholar at the University of Chicago Data Science Institute working at the intersection of physics and machine learning. My research interests include physics-informed machine learning, condensed matter physics, and nonlinear dynamics, and I am more broadly interested in developing new computational methods for modeling and understanding physical systems with an emphasis on incorporating physics-informed priors and developing interpretable representation learning methods.
I received a Ph.D. in Physics from MIT in 2022, and an A.B. in Physics and Mathematics from Harvard in 2016.
|Feb 16, 2022||APS March Meeting 2022|
- Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional StructureTransactions of Machine Learning Research (2022)
- Discovering Conservation Laws using Optimal Transport and Manifold Learning(2022)
- Discovering sparse interpretable dynamics from partial observationsCommunications Physics (2022)
- Discovering Dynamical Parameters by Interpreting Echo State NetworksNeurIPS 2021 AI for Science Workshop (2021) — Best Paper Award
- Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific DiscoveryIEEE Transactions on Neural Networks and Learning Systems (2021)
- Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised LearningPhysical Review X (2020)
- Energy Loss at Propagating Jamming Fronts in Granular Gas ClustersPhysical Review Letters (2013)