Stanford University
Showing 27,501-27,550 of 37,022 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. -
Raheleh Roudi
Basic Life Research Scientist, Rad/Pediatric Radiology
BioRaheleh Roudi is a research scientist in the Department of Radiology at Stanford University. Dr. Roudi trained at the Iran University of Medical Sciences, Iran. She worked as an Assistant Professor at the Iran University of Medical Sciences, Iran from 2015 to 2019, before coming to the United States. During this time, Dr. Roudi worked on several projects which have led to successful collaborations with the Karolinska Institute; Charite Universitatsmedizin Berlin; Oslo University Hospital; National University of Singapore; Shanghai University of Traditional Chinese Medicine and University of Brescia, among other internationally recognized institutions.
Dr. Roudi was a visiting scientist in the University of Texas at San Antonio and then appointed as a postdoctoral associate at the University of Minnesota for one year, before joining Stanford University in 2022.
Her research interest focuses on the molecular oncology and immunotherapies of solid tumors and she published more than 40 peer reviewed papers. -
Dara Rouholiman
Affiliate, Anesthesia - Adult Pain Medicine
BioDara Rouholiman is a machine learning research engineer at Stanford AIM Lab, where he develops and evaluates predictive and generative models for anesthesia and perioperative medicine. His current research focuses on LLM evaluation in clinical settings, deep learning for time-series forecasting, and ML-driven perioperative risk prediction using electronic health records.
His work on tool-augmented LLMs for clinical calculations was published in Nature's npj Digital Medicine (2025). Previously, he led ML development at COR, an at-home blood-monitoring device startup (3 patents filed), and co-founded Telesphora, whose opioid overdose prediction model was deployed with the Connecticut Department of Public Health. He holds a B.S. in physical chemistry from UC Santa Cruz and serves as Lead Instructor for Stanford SASI's Healthcare Innovation Internship. -
Alex Rousina-Webb
Research Technical Manager, SLAC National Accelerator Laboratory
Current Role at StanfordAt SLAC National Accelerator Laboratory, I lead the lab-to-market pipeline for SLAC innovations. My role includes collaborating with Stanford University’s tech transfer program, managing DOE technology transfer initiatives like OTT and SBIR/STTR, and handling SLAC’s intellectual property portfolio. I work closely with the Stanford Office of Technology Licensing to drive innovation from discovery to commercialization.
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Larissa Roux MD PhD
Adjunct Professor, Medicine - Primary Care and Population Health
BioLarissa Roux is a sport medicine physician and health economist. She completed medical school at the University of Alberta, and followed this with residency training in family medicine and a fellowship in primary care sport medicine at the University of Calgary, as well as advanced training in lifestyle medicine. She combined her clinical training with a master’s in public health at Harvard, and a PhD in health economics at the University of Calgary. Her interest in public health and health policy resulted in a post-doctoral fellowship at the US Centers for Disease Control, in Atlanta in the Division of Nutrition and Physical Activity. Although she has deeply enjoyed working with athletes and dancers, her main clinical interest has been in the optimization of human performance in patients with chronic conditions, including obesity, arthritis, and trauma. Her academic and health policy work has focused on the economic evaluation of competing therapies for obesity, and population-level physical activity promotion strategies in the US and around the world. Larissa's interest in data science and technology applications in global health contributed to an ongoing health information technology venture. She believes that innovative, tailored, multidisciplinary, and multimodal approaches to chronic disease have transformative potential in human health.
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Corey Rovzar
Instructor, Medicine - Stanford Prevention Research Center
Current Research and Scholarly InterestsEnhancing human movement through scalable, remotely delivered physical activity interventions, remote assessment and monitoring of human movement, health technology development, fall prevention, aging, digital balance assessment, improving access to health and healthcare, increasing healthspan, lifestyle medicine
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Jenny Roxas
Associate Director of Belonging, Access, & Career Equity (BACE), BEAM, Stanford Career Education
Current Role at StanfordAssociate Director of Career Coaching and Education - Belonging, Access, & Career Equity (BACE)
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Anuradha Roy
Casual - Non-Exempt, Anesthesia - Adult Pain Medicine
BioAnuradha (Anu) Roy is the Project Manager for the NCCIH R01 Grant, Single Session Pain Catastrophizing Treatment Efficacy and Mechanisms Trial in Department of Anesthesiology, Perioperative, and Pain Medicine at Stanford Medical School. Originally from the East Coast, she received her BS in Biomedical Engineering from the University of Virginia and her MSc in Public Health in Developing Countries from London School of Hygiene & Tropical Medicine. After years of working abroad in global public health, her research interest transitioned to the public health impact of chronic health conditions - in particular, chronic pain - and understanding the intersection of mind’s impact on health and well-being. For the past three years, on the R01, she has been running day-to-day operations and management of study recruitment, data collection/pre-processing, and reporting. In the near future, she plans to pursue a PhD in Clinical Psychology. In her spare time, Anu enjoys home/exterior design projects, spending time with her partner and dog, creating community, and finding the best food in the San Francisco Bay Area.
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Mohana Roy, MD
Clinical Assistant Professor, Medicine - Oncology
BioDr. Roy is a medical oncologist and a clinical assistant professor in the Stanford University School of Medicine Department of Medicine, Division of Medical Oncology. She has expertise in Lung and Thoracic cancers, but with a broad clinical interest in oncology, including in Carcinoma of Unknown Primary (CUP).
Dr. Roy became an oncologist because of her passion for patient care. She is committed to being a clinician and is focused on improving the patient experience, given how the complex process of getting cancer care can be made a bit more seamless. She is the Associate Medical Director for Quality at Stanford Cancer Center from 2022.
She had led major efforts in the cancer program including starting standardized discharge follow up for patients after hospitalization, starting same day clinical care at the cancer center, and also expediting care for patients with an unclear diagnosis of cancer but with suspected imaging concerns.
Her research interests include access to clinical trials, quality improvement and improving care delivery. In that effort, she has published on work regarding patient reported outcomes (PROs), through distress screening with the Stanford Medicine Cancer Center, and in care for patient with limited English proficiency.
Dr. Roy received her medical degree from Albert Einstein College of Medicine, and then completed residency training at Beth Israel Deaconess Medical Center. She then completed fellowship training in Hematology and Oncology at Stanford, where she was chief fellow. -
Soumyadeep Roy
Postdoctoral Scholar, Biomedical Informatics
BioI am a postdoctoral scholar at the Center for Biomedical Informatics Research of Stanford University, advised by Prof. Tina Hernandez-Boussard.
My primary area of research is natural language processing, with expertise in medical and healthcare applications. My research areas of interest are Foundation Models for Medicine, Generative AI, Text Summarization, and Efficient Pretraining.
I hold a PhD in Computer Science and Engineering from the Indian Institute of Technology Kharagpur, where I worked with Prof. Niloy Ganguly and Prof. Shamik Sural. Here, I was part of the Complex Networks Research Group (CNeRG). My PhD thesis is titled “Domain Adaptation for Medical Language Understanding”, where I developed novel domain adaptation techniques to effectively and efficiently adapt open-domain AI models to the medical domain.
In summary, I have six years of experience working with medical NLP data, which includes clinical trial registry data (2018-2021), medical forum questions (2020-2021), DNA sequence data (2021-2024), biomedical scientific literature (2023 - 2025), clinical data (2021-2023) and EHR clinical notes (2025). My medical AI research experience includes 2.5 years at L3S Research Germany collaborating with Hannover Medical School as well as a 7-month research internship at GE HealthCare Technology and Innovation Center (HTIC) in Bangalore, India. I also presented a tutorial on March 10, 2025 titled "Building Trustworthy AI Models for Medicine" at WSDM 2025 held in Germany.
In my free time, I like hiking, and playing chess or table tennis.
