School of Medicine
Showing 51-60 of 62 Results
Research Engineer, Biomedical Data Science
Current Role at StanfordPrimarily collaborating with VA researchers on GWAS using MVP data. Also providing statistical support to some Stanford clinicians conducting investigational research.
Jaap Suermondt, PhD
Executive Director, Biomedical Informatics Training Program, Biomedical Data Science
BioI’m the Executive Director of Stanford’s Biomedical Informatics Program. I am also an advisor to Stanford’s Population Health Sciences Program and participating faculty in Stanford’s Clinical Informatics Fellowship Program. I’m active in the Silicon Valley ecosystem as a mentor to startup founders, occasional investor, and strategic advisor -- all this obviously while being observant of and compliant with Stanford's policies and conflict of interest and commitment constraints. Throughout my career, I have been an operational and strategic executive with a track record in applied innovation and deep industry, GTM, and technical expertise.
Instructor, Biomedical Data Science
BioSuzanne Tamang is based at the Center for Population Health Sciences She received her Ph.D. in Computer Science from the City University of New York and completed her postdoctoral training at the Stanford's Center for Biomedical Bioinformatics.
At Stanford, Suzanne's collaborations span the Alcoa Research Consortium, the Clinical Excellence Research Center and the Stanford Cancer Institute. She is also affiliated with the Department of Rheumatology at UCSF.
John S. Tamaresis, PhD, MS
Biostatistician, Biomedical Data Science
BioDr. Tamaresis joined the Stanford University School of Medicine in Summer 2012. He earned the Ph.D. in Applied Mathematics from the University of California, Davis and received the M.S. in Statistics from the California State University, East Bay. He has conducted research in computational biology as a postdoctoral scholar at the University of California, Merced and as a biostatistician at the University of California, San Francisco.
As a statistician, Dr. Tamaresis has developed and validated a highly accurate statistical biomarker classifier for gynecologic disease by applying multivariate techniques to a large genomic data set. His statistical consultations have produced data analyses for published research studies and analysis plans for novel research proposals in grant applications. As an applied mathematician, Dr. Tamaresis has created computational biology models and devised numerical methods for their solution. He devised a probabilistic model to study how the number of binding sites on a novel therapeutic molecule affected contact time with cancer cells to advise medical researchers about its design. For his doctoral dissertation, he created and analyzed the first mathematical system model for a mechanosensory network in vascular endothelial cells to investigate the initial stage of atherosclerotic disease.
Professor of Biomedical Data Science and, by courtesy, of Statistics
Current Research and Scholarly InterestsMy research interest includes
(1) Survival Analysis and Semiparametric Modeling;
(2) Resampling Method ;
(3) Meta Analysis ;
(4) High Dimensional Data Analysis;
(5) Personalized Medicine for Disease Diagnosis, Prognosis and Treatment.
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsMy research is in applied statistics and biostatistics. I specialize in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis.
Associate Professor of Pediatrics (Systems Medicine), of Biomedical Data Science and, by courtesy, of Psychiatry and Behavioral Sciences
Current Research and Scholarly InterestsSystems biology for design of clinical solutions that detect and treat disease
Wing Hung Wong
Stephen R. Pierce Family Goldman Sachs Professor in Science and Human Health and Professor of Biomedical Data Science
Current Research and Scholarly InterestsCurrent interest centers on the application of statistics to biology and medicine. We are particularly interested in questions concerning gene regulation, genome interpretation and their applications to precision medicine.