Peter Y. Lu
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 representation learning with applications in nonlinear dynamics, quantum physics, fluid dynamics, 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 that lead to new scientific insights.
I received an A.B. in Physics and Mathematics from Harvard in 2016 and a Ph.D. in Physics from MIT in 2022, advised by Marin Soljačić.
Selected Publications
- Discovering Dynamical Parameters by Interpreting Echo State NetworksNeurIPS 2021 AI for Science Workshop (2021) — Best Paper Award