Research/Areas of Interest
Scientific machine learning: physics-informed ML, representation learning, generative modeling, interpretability;
Complex systems: nonlinear dynamics, chaos, interacting quantum systems, materials science, fluid turbulence
Website: https://petery.lu
Education
- Ph.D., Physics, Massachusetts Institute of Technology, Cambridge, United States, 2022
- A.B., Physics and Mathematics, Harvard University, Cambridge, United States, 2016
Biography
Peter Y. Lu is an Assistant Professor in the Department of Electrical and Computer Engineering at Tufts University, working at the intersection of physics and machine learning. Prior to joining Tufts, Peter was an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow at the University of Chicago. He received a Ph.D. in Physics from MIT in 2022, where he was an NDSEG Fellow, and an A.B. in Physics and Mathematics from Harvard in 2016. He was recognized as a "Rising Star in Data Science" by UC San Diego, the University of Chicago, and Stanford University in 2024.
Peter develops foundational machine learning methods for modeling and understanding complex physical systems with an emphasis on identifying relevant physical features, accelerating expensive simulations, solving inverse problems, and incorporating physical priors and constraints. His research interests include physics-informed machine learning, interpretable representation learning, and scientific generative modeling with applications to nonlinear dynamics and chaos, interacting quantum systems, materials science, fluid turbulence, and other areas.
Peter develops foundational machine learning methods for modeling and understanding complex physical systems with an emphasis on identifying relevant physical features, accelerating expensive simulations, solving inverse problems, and incorporating physical priors and constraints. His research interests include physics-informed machine learning, interpretable representation learning, and scientific generative modeling with applications to nonlinear dynamics and chaos, interacting quantum systems, materials science, fluid turbulence, and other areas.