Grant M. Rotskoff

Grant M. Rotskoff is an Assistant Professor of Chemistry in the Department of CHEM at Stanford University. His research focuses on Physical Chemistry and Theoretical Chemistry, with an emphasis on the nonequilibrium dynamics of living matter. He studies self-organization across molecular to cellular scales, developing theoretical and computational tools to probe and predict properties of physical systems driven away from equilibrium. His work includes characterizing and designing physically accurate machine learning techniques for biophysical modeling. Prior to joining Stanford, he was a James S. McDonnell Fellow at the Courant Institute of Mathematical Sciences at New York University. He completed his Ph.D. at the University of California, Berkeley, in the Biophysics graduate group. His research interests include mesoscale biophysics, machine learning for physical models, and methods for nonequilibrium simulation and control.

Grant M. Rotskoff is a researcher in Scientific Machine Learning and Biophysics, focusing on the intersection of machine learning and physical systems. His work explores the trainability of neural networks, nonequilibrium dynamics, and the application of statistical mechanics to biological processes. He has published on topics such as adaptive Monte Carlo methods, nanocrystal transformations, and optimal control in nonequilibrium systems. His research also includes the use of machine learning for structural elucidation from NMR data and the study of protein dynamics in signaling systems. Rotskoff's contributions span both theoretical and applied aspects of computational biophysics and machine learning.