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

Researchers

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Abigail Park

Graduate Student

Coarse-graining, Intrinsically Disordered Proteins, Machine Learning

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Grant M. Rotskoff

Assistant Professor of Chemistry

Nonequilibrium Dynamics, Biophysics, Machine Learning, theory and practice

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Andy Mitchell

Graduate Student

Driven Sampling, Transition States and Committors, Machine Learning

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Clay Batton

Postdoctoral Researcher

Coarse Graining, Nonequilibrium Control

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Emmit Pert

Graduate Student

Molecular Dynamics, Importance Sampling

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Jérémie Klinger

Postdoctoral Researcher

Nonequilibrium dynamics, Optimal control theory

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Nick Juntunen

Graduate Student

Nanoparticles, Inorganic Materials

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Sebastian Ibararran

Graduate Student

Molecular Transformers, Physics-informed Diffusion Models

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Sherry Li

Graduate Student

Machine Learning, Enhanced Sampling Methods

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Shriram Chennakesavalu

Graduate Student

Machine Learning, Nonequilibrium Control

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Sreekanth Manikandan

Postdoctoral Researcher

Nonequilibrium Control, Biophysical Self-Assembly

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Steven Dunne

Graduate Student

Monte Carlo Methods, Intrinsically Disordered Proteins, Machine Learning

Alumni

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David Toomer

Undergraduate Researcher

Machine Learning

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Isaac Applebaum

Undergraduate Researcher

Machine Learning, CARTs, (joint with Waymouth Group)

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Jiawei Yan

Postdoctoral Researcher

Self-assembly, Complex networks

Recent & Upcoming Talks

Recent Advances in Physics-Informed Machine Learning

Machine learning (ML) is spurring a transformation in the computational sciences by providing a new way to build flexible, universal, and efficient approximations for complex high-dimensional functions and functionals. One area in which the impact of these new tools is beginning to be understood is the physical sciences, where traditionally intractable high-dimensional partial differential equations are now within reach. This tutorial will explore how developments in ML complement computational problems in the physical sciences, with a particular focus on solving partial differential equations, where the challenges of high-dimensionality and data acquisition also arise. The first important example this tutorial will cover is using Deep Learning Methods for solving high-dimensional PDEs, which have wide application in variational rare events calculations, many-body quantum systems, and stochastic control. Another challenge covered by this tutorial that researchers often face is the complexity or lack of specification of the models they are using when performing uncertainty quantification. Thus another line of research aims to recover the underlying dynamic using observational data. This tutorial will introduce the well-developed methods and theories for using machine learning in scientific computing. We will first discuss how to incorporate physical priors into machine learning models. Next, we will discuss how these methods can help to solve challenging physical and chemical problems. Finally, we will discuss the statistical and computational theory for scientific machine learning. In this tutorial, we will not focus on the technical details behind these theories, but on how they can help the audience to understand the challenges of using machine learning in differential equation applications and to develop new methods for addressing these challenges.

Contact

Please note: I cannot respond to individual inquiries about PhD opportunities. If you are interested in a PhD at Stanford, I typically work with students admitted to the Chemistry, Biophysics, or ICME PhD programs.

email: rotskoff at stanford

phone: +1 (650) 382-3815

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

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