Natural Sciences
Showing 261-280 of 382 Results
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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.
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Joseph Romano
Professor of Statistics and of Economics
Current Research and Scholarly InterestsWork in progress is described under "Projects"
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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
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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. -
Chiara Sabatti
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsStatistical models and reasoning are key to our understanding of the genetic basis of human traits. Modern high-throughput technology presents us with new opportunities and challenges. We develop statistical approaches for high dimensional data in the attempt of improving our understanding of the molecular basis of health related traits.
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Julia Salzman
Associate Professor of Biomedical Data Science, of Biochemistry and, by courtesy, of Statistics and of Biology
On Leave from 09/01/2025 To 06/01/2026Current Research and Scholarly Interestsstatistical computational biology focusing on splicing, cancer and microbes
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Robert Sapolsky
John A. and Cynthia Fry Gunn Professor, Professor of Biology, of Neurology and Neurological Sciences and of Neurosurgery
Current Research and Scholarly InterestsNeuron death, stress, gene therapy
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Monika Schleier-Smith
Associate Professor of Physics
Current Research and Scholarly InterestsIn between the few-particle realm where we have mastered quantum mechanics and the macroscopic domain describable by classical physics, there lies a broad swath of territory where quantum effects are relevant but still largely out of our control and partly beyond our comprehension. This territory includes metrological instruments whose precision is limited by the quantum projection noise of millions of atoms; and materials whose bulk properties emerge from many-body interactions intractable to simulation on classical computers. Professor Schleier-Smith’s research aims to advance our control and understanding of many-particle quantum systems by engineering new quantum states and Hamiltonians with ensembles of laser-cooled atoms.
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Mark J. Schnitzer
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences and Professor of Biology, of Applied Physics and of Neurosurgery
Current Research and Scholarly InterestsThe goal of our research is to advance experimental paradigms for understanding normal cognitive and disease processes at the level of neural circuits, with emphasis on learning and memory processes. To advance these paradigms, we invent optical brain imaging techniques, several of which have been widely adopted. Our neuroscience studies combine these imaging innovations with behavioral, electrophysiological, optogenetic and computational methods, enabling a holistic approach to brain science.
