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