School of Medicine
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Instructor, Biomedical Data Science
Bio Suzanne 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.
Professor of Biomedical Data Science and, by courtesy, of Statistics
Current Research and Scholarly Interests My research interest includes
(1) Survival Analysis and Semiparametric Modeling;
(2) Resampling Method ;
(3) Meta Analysis ;
(4) High Dimensional Data Analysis;
(5) Precision Medicine for Disease Diagnosis, Prognosis and Treatment.
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly Interests My 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 Interests Systems 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 Interests Current 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.
Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering
Current Research and Scholarly Interests My group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.
We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.