School of Humanities and Sciences


Showing 1-20 of 21 Results

  • Srinivas Raghu

    Srinivas Raghu

    Professor of Physics

    BioI am interested in the emergent behavior of quantum condensed matter systems. Some recent research topics include non-Fermi liquids, quantum criticality, statistical mechanics of strongly interacting and disordered quantum systems, physics of the half-filled Landau level, quantum Hall to insulator transitions, superconductor-metal-insulator transitions, and the phenomenology of quantum materials.

    Past contributions that I'm particularly proud of include the co-founding of the subject of topological photonics (with Duncan Haldane), scaling theories of non-Fermi liquid metals (with Shamit Kachru and Gonzalo Torroba), Euclidean lattice descriptions of Chern-Simons matter theories and their dualities in 2+1 dimensions (with Jing-Yuan Chen and Jun Ho Son), and 'dual' perspectives of quantum Hall transitions (with Prashant Kumar and Michael Mulligan).

  • Jianghong Rao

    Jianghong Rao

    Professor of Radiology (Molecular Imaging Program at Stanford) and, by courtesy, of Chemistry

    Current Research and Scholarly InterestsProbe chemistry and nanotechnology for molecular imaging and diagnostics

  • Kristy Red-Horse

    Kristy Red-Horse

    Professor of Biology

    Current Research and Scholarly InterestsCardiovascular developmental biology

  • Seung Yon Rhee

    Seung Yon Rhee

    Professor (By Courtesy), Biology

    BioSeung Yon (Sue) Rhee is a Senior Staff Member of Plant Biology Department at Carnegie Institution for Science and Professor (by courtesy) in Biology Department, Stanford University. Her group strives to uncover molecular mechanisms underlying adaptive traits in the face of heat, drought, nutrient limitation, and pests. Dr. Rhee’s group studies a variety of plants including models, crops, medicinal and desert plants. Her group employs computational modeling and targeted laboratory testing to study mechanisms of adaptation, functions of novel genes, organization and function of metabolic networks, and chemical and neuronal code of plant-animal interactions. Her group is also interested in developing translational research programs involving biomass maximization under drought in bioenergy crops. More recently, Dr. Rhee has spearheaded a grassroots community building effort called the Plant Cell Atlas initiative, which strives to map all the molecular determinants of plant cells in order to understand and engineer them. Dr. Rhee received her B.A. in biology from Swarthmore College in 1992 and a Ph.D. in biology from Stanford University in 1997. She has been an investigator at Carnegie’s Plant Biology Department since 1999.

  • Thomas Rogerson

    Thomas Rogerson

    Basic Life Research Scientist

    Current Research and Scholarly InterestsAs a postdoctoral research fellow in the laboratory of Mark Schnitzer I am utilizing chronic, in vivo, fluorescence calcium-imaging combined with chemo and optogenetic manipulations to determine the mechanisms by which neuronal circuits and the ensembles of cells within them enable the encoding and recall of context-dependent memories.

  • Joseph Romano

    Joseph Romano

    Professor of Statistics and of Economics

    Current Research and Scholarly InterestsWork in progress is described under "Projects"

  • Noah Rosenberg

    Noah Rosenberg

    Stanford Professor of Population Genetics and Society

    Current Research and Scholarly InterestsHuman evolutionary genetics, mathematical models in evolution and genetics, mathematical phylogenetics, statistical and computational genetics, theoretical population genetics

  • Grant M. Rotskoff

    Grant M. Rotskoff

    Assistant Professor of Chemistry

    BioGrant Rotskoff studies the nonequilibrium dynamics of living matter with a particular focus on self-organization from the molecular to the cellular scale. His work involves developing theoretical and computational tools that can probe and predict the properties of physical systems driven away from equilibrium. Recently, he has focused on characterizing and designing physically accurate machine learning techniques for biophysical modeling. Prior to his current position, Grant was a James S. McDonnell Fellow working 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 supported by an NSF Graduate Research Fellowship. His thesis, which was advised by Phillip Geissler and Gavin Crooks, developed theoretical tools for understanding nonequilibrium control of the small, fluctuating systems, such as those encountered in molecular biophysics. He also worked on coarsegrained models of the hydrophobic effect and self-assembly. Grant received an S.B. in Mathematics from the University of Chicago, where he became interested in biophysics as an undergraduate while working on free energy methods for large-scale molecular dynamics simulations.

    Research Summary

    My research focuses on theoretical and computational approaches to "mesoscale" biophysics. Many of the cellular phenomena that we consider the hallmarks of living systems occur at the scale of hundreds or thousands of proteins. Processes like the self-assembly of organelle-sized structures, the dynamics of cell division, and the transduction of signals from the environment to the machinery of the cell are not macroscopic phenomena—they are the result of a fluctuating, nonequilibrium dynamics. Experimentally probing mesoscale systems remains extremely difficult, though it is continuing to benefit from advances in cryo-electron microscopy and super-resolution imaging, among many other techniques. Predictive and explanatory models that resolve the essential physics at these intermediate scales have the power to both aid and enrich the understanding we are presently deriving from these experimental developments.

    Major parts of my research include:

    1. Dynamics of mesoscale biophysical assembly and response.— Biophysical processes involve chemical gradients and time-dependent external signals. These inherently nonequilibrium stimuli drive supermolecular organization within the cell. We develop models of active assembly processes and protein-membrane interactions as a foundation for the broad goal of characterizing the properties of nonequilibrium biomaterials.

    2. Machine learning and dimensionality reduction for physical models.— Machine learning techniques are rapidly becoming a central statistical tool in all domains of scientific research. We apply machine learning techniques to sampling problems that arise in computational chemistry and develop approaches for systematically coarse-graining physical models. Recently, we have also been exploring reinforcement learning in the context of nonequilibrium control problems.

    3. Methods for nonequilibrium simulation, optimization, and control.— We lack well-established theoretical frameworks for describing nonequilibrium states, even seemingly simple situations in which there are chemical or thermal gradients. Additionally, there are limited tools for predicting the response of nonequilibrium systems to external perturbations, even when the perturbations are small. Both of these problems pose key technical challenges for a theory of active biomaterials. We work on optimal control, nonequilibrium statistical mechanics, and simulation methodology, with a particular interest in developing techniques for importance sampling configurations from nonequilibrium ensembles.