Vice Provost and Dean of Research
Showing 2,011-2,020 of 2,496 Results
-
Kawin Setsompop
Professor of Radiology (Radiological Sciences Laboratory) and, by courtesy, of Electrical Engineering
BioKawin Setsompop is a Professor of Radiology and, by courtesy, of Electrical Engineering. His research focuses on the development of novel MRI acquisition methods, with the goal of creating imaging technologies that can be used to help better understand brain structure and function for applications in Healthcare and Health sciences. He received his Master’s degree in Engineering Science from Oxford University and his PhD in Electrical Engineering and Computer Science from MIT. Prior to joining Stanford, he was a postdoctoral fellow and subsequently a faculty at the A.A. Martinos center for biomedical imaging, MGH, as well as part of the Harvard and MIT faculty. His group has pioneered several widely-used MRI acquisition technologies, a number of which have been successfully translated into FDA-approved clinical products on Siemens, GE, Phillips, United Imaging and Bruker MRI scanners worldwide. These technologies are being used daily to study the brain in both clinical and neuroscientific fields.
-
Ross Shachter
Associate Professor of Management Science and Engineering
Current Research and Scholarly InterestsProf. Shachter's research has focused on the representation, manipulation, and analysis of uncertainty and probabilistic reasoning in decision systems. As part of this work, he developed the DAVID influence diagram processing system for the Macintosh. He has developed models scheduling patients for cancer follow-up, and analyzing vaccination strategies for HIV and Helobacter pylori.
-
Robert W. Shafer
Professor (Research) of Medicine (Infectious Diseases) and, by courtesy, of Pathology
Current Research and Scholarly InterestsMy group’s research is on the mechanisms and consequences of virus evolution with a focus on HIV therapy and drug resistance. We maintain a public HIV drug resistance database (http://hivdb.stanford.edu) as a resource for HIV drug resistance surveillance, interpreting HIV drug resistance tests, and HIV drug development. Our paramount goal is to inform HIV treatment and prevention policies by identifying the main factors responsible for the emergence and spread of drug resistance.
-
Nigam H. Shah, MBBS, PhD
Professor of Medicine (Computational Medicine), of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsWe answer clinical questions using aggregate patient data at the bedside. The Informatics Consult Service (https://greenbutton.stanford.edu/) put this idea in action and led to the creation of Atropos Health. We build predictive models that allow taking mitigating actions, keeping the human in the loop.
-
Nirao Shah
Professor of Psychiatry and Behavioral Sciences (Major Laboratories and Clinical Translational Neurosciences Incubator), of Neurobiology and, by courtesy, of Obstetrics and Gynecology
Current Research and Scholarly InterestsWe study how our brains generate social interactions that differ between the sexes. Such gender differences in behavior are regulated by sex hormones, experience, and social cues. Accordingly, we are characterizing how these internal and external factors control gene expression and neuronal physiology in the two sexes to generate behavior. We are also interested in understanding how such sex differences in the healthy brain translate to sex differences in many neuro-psychiatric illnesses.
-
Mohammad Shahrokh Esfahani
Assistant Professor of Radiation Oncology (Radiation and Cancer Biology)
BioI lead a computational oncology laboratory that develops machine learning and statistical methods for high-dimensional genomics, with particular expertise in Bayesian and uncertainty-aware modeling to integrate prior biological knowledge with large-scale datasets.
Our research centers on liquid biopsy analytics—especially cell-free DNA (cfDNA)—to noninvasively quantify genetic and epigenetic states relevant to cancer detection, monitoring, and tumor evolution. We developed EPIC-seq, a fragmentomics-based method that uses cfDNA fragmentation patterns to infer regulatory activity and gene expression programs, providing a scalable framework for epigenetic profiling from blood.
A core methodological focus of the lab is enabling reliable inference in extremely low signal-to-noise settings that are typical of cfDNA and early-stage disease. We build robust, interpretable models and benchmarking frameworks that support clinical translation, with the long-term aim of democratizing access to sensitive, minimally invasive cancer diagnostics.
