77 Massachusetts Ave, Bldg 6C-411

Cambridge, MA 02139

I am a physics Ph.D. student in Marin Soljačić’s group at MIT. I work at the intersection of physics and machine learning, and my research interests include physics-informed machine learning, condensed matter physics, nonlinear dynamics, and photonics. I am more broadly interested in developing new computational methods for simulating and understanding physical systems with an emphasis on incorporating physics-based priors and providing interpretability.

I received my A.B. in Physics and Mathematics from Harvard College in 2016 and am currently pursuing a Ph.D. in Physics at MIT.

## Selected Publications

1. Discovering Dynamical Parameters by Interpreting Echo State Networks
NeurIPS 2021 AI for Science Workshop (2021) — Best Paper Award
2. Discovering Sparse Interpretable Dynamics from Partial Observations
(2021)
3. Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
(2021)
4. Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
Physical Review X (2020)
5. Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
IEEE Transactions on Neural Networks and Learning Systems (2021)