Our Research

Nonequilibrium Dynamics and Control in Biological Systems

Biology is influenced by processes from the quantum to the macroscopic scale, a fundamental challenge to studying biophysical dynamics. We study the time-evolution of biological systems at mesoscales, where the molecular meets the macroscopic.

Rigorous Applications of Machine Learning to Computational Chemistry

We use techniques from statistical inference and machine learning to sample, coarsegrain, and interpret complex chemical and biophysical models. We also characterize and analyze numerical methods to guarantee accuracy and robustness.

Self-Assembly in and out of Equilibrium

We study the self-organization of biophysical and chemical species as dictate by intrinsic material properties. Additionally, we are interested in analyzing self-assembly in nonequilibrium conditions as well as exploiting external control to dictate the outcomes of an assembly process.

The Group



Shriram Chennakesavalu

Graduate Student

Machine Learning, Nonequilibrium Control


Grant M. Rotskoff

Assistant Professor of Chemistry

Nonequilibrium Dynamics, Biophysics, Machine Learning, theory and practice


Andy Mitchell

Graduate Student

Driven Sampling, Transition States and Committors, Machine Learning


David Toomer

Undergraduate Researcher

Machine Learning


Emmit Pert

Graduate Student

Molecular Dynamics, Importance Sampling


Isaac Applebaum

Undergraduate Researcher

Machine Learning, CARTs, (joint with Waymouth Group)


Joseph Lucero

Graduate student

Nonequilibrium thermodynamics and control, Pattern formation


Sherry Li

Graduate Student

Machine Learning, Enhanced Sampling Methods

Recent & Upcoming Talks


email: rotskoff at stanford

phone: +1 (650) 382-3815

physical coordinates: 165 Keck Science Building, Stanford, CA 94305

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