Vice Provost and Dean of Research
Showing 1,891-1,900 of 2,457 Results
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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. -
Scott Rozelle
Helen C. Farnsworth Professor of International Agricultural Policy and Senior Fellow at the Stanford Institute for Economic Policy Research
Current Research and Scholarly InterestsThemes related to China, especially agricultural policy, the emergence and evolution of markets and other economic institutions, and the economics of poverty and inequality.
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Daniel Rubin
Professor of Biomedical Data Science and of Radiology (Integrative Biomedical Imaging Informatics at Stanford), Emeritus
Current Research and Scholarly InterestsMy research interest is imaging informatics--ways computers can work with images to leverage their rich information content and to help physicians use images to guide personalized care. Work in our lab thus lies at the intersection of biomedical informatics and imaging science.
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Sam Rubin
Licensing Manager, Life Sciences, Office of Technology Licensing (OTL)
BioSam Joined OTL in September 2022 as a Licensing Associate on the Life Sciences team. Prior to joining, Sam worked in various business-focused roles in the life science industry. He has experience negotiating and executing services agreements for complex drug development collaborations, as well as technology licensing agreements with industry partners.
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Daniel Rugar
Affiliate, Ginzton, E.L. Laboratory
Visiting Scholar, Ginzton, E.L. LaboratoryBioDaniel Rugar is visiting scholar at Stanford University working with Professor Amir Safavi-Naeini. He retired from IBM Research in 2024, finishing as Principal Research Scientist and Manager of Exploratory Quantum Devices at the IBM Almaden Research Center in San Jose, California. He previously served as an IBM Distinguished Research Staff Member and Manager of Nanoscale Studies.
Dr. Rugar received his B.A. in Physics from Pomona College in 1975 and his Ph.D. in Applied Physics from Stanford University in 1982. He was co-recipient of the Günther Laukien Prize for the development of magnetic resonance force microscopy and was awarded the Cozzarelli Prize from the Proceedings of the National Acacademy of Sciences. He has served as a Distinguished Lecturer of the IEEE Magnetic Society. More recently, he was awarded the 2022 APS Keithley Award in Measurement Science. Dr. Rugar is a fellow of the American Physical Society (APS), the American Association for the Advancement of Science (AAAS) and the Institute of Electrical and Electronic Engineers (IEEE). -
Ahmad Rushdi
Director of Industry Programs, Institute for Human-Centered Artificial Intelligence (HAI)
BioAhmad A. Rushdi, PhD, is the director of HAI industry programs—research collaborations and executive education—at Stanford’s Institute for Human-Centered AI (HAI), translating cutting-edge research into responsible, deployable solutions for global enterprises. He forges durable bridges between Stanford scholars and industry to advance trustworthy, real-world AI.
Ahmad's own research focuses on uncertainty quantification and statistical signal processing for AI/ML systems. Previously, he held R&D roles at Sandia National Labs, Northrop Grumman, UC Davis, UT Austin, and Cisco. He earned a PhD in Electrical & Computer Engineering from UC Davis and MS/BS degrees in Electrical Engineering from Cairo University. -
Mirabela Rusu
Assistant Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Biomedical Data Science and of Urology
Current Research and Scholarly InterestsDr. Mirabela Rusu focuses on developing analytic methods for biomedical data integration, with a particular interest in radiology-pathology fusion. Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization.
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Florentine Rutaganira
Assistant Professor of Biochemistry and of Developmental Biology
Current Research and Scholarly InterestsWe use chemical tools to decipher the roles of key signaling networks in choanoflagellates, single-celled organisms that are the closest living relatives of animals. Choanoflagellates produce molecular signals essential for intercellular communication in animals and the presence of these molecules in choanoflagellates suggests that signaling components needed to communicate between cells is evolutionarily ancient. We aim to uncover new understanding of animal development, physiology and disease.
