PETER Y

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 representational learning with applications in nonlinear dynamics, condensed matter physics, photonics, fluid dynamics, biophysics, 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... I received a Ph.D. in Physics from MIT in 2022, and an A.B. in Physics and Mathematics from Harvard in 2016... Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box. The data may have some known structure (e.g. symmetries) and/or the data generation process may be a composite process that..

Also known as: Peter