School of Medicine


Showing 31-36 of 36 Results

  • Lu Tian

    Lu Tian

    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) Precision Medicine for Disease Diagnosis, Prognosis and Treatment.

  • Robert Tibshirani

    Robert Tibshirani

    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.

  • Dennis Wall

    Dennis Wall

    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

  • John Witte

    John Witte

    Professor of Epidemiology and Population Health, of Biomedical Data Science and, by courtesy, of Genetics

    BioDr. Witte joined the Stanford community in July 2021. In addition to serving as Vice Chair and professor in the Department of Epidemiology & Population Health, and as a professor of Biomedical Data Science and, by courtesy, of Genetics, he will also serve as a member of the Stanford Cancer Institute.

    Dr. Witte is an internationally recognized expert in genetic epidemiology. His scholarly contributions include deciphering the genetic and environmental basis of prostate cancer and developing widely used methods for the genetic epidemiologic study of disease. His prostate cancer work has used comprehensive genome-wide studies of germline genetics, transcriptomics, and somatic genomics to successfully detect novel variants underlying the risk and aggressiveness of this common disease. A key aspect of this work has been distinguishing genetic factors that may drive increased prostate cancer risk and mortality among African American men. Providing an avenue to determine which men are more likely to be diagnosed with clinically relevant prostate cancer and require additional screening or specific treatment can help reduce disparities in disease prevalence and outcomes across populations. Dr. Witte has also developed novel hierarchical and polygenic risk score modeling for undertaking genetic epidemiology studies. These advances significantly improve our ability to detect disease-causing genes and to translate genetic epidemiologic findings into medical practice.

    Dr. Witte has received the Leadership Award from the International Genetic Epidemiology Society (highest award), and the Stephen B. Hulley Award for Excellence in Teaching. His extensive teaching portfolio includes a series of courses in genetic and molecular epidemiology. He has mentored over 50 graduate students and postdoctoral fellows, serves on the executive committees of multiple graduate programs, and has directed a National Institutes of Health funded post-doctoral training program in genetic epidemiology for over 20 years. Recently appointed to the National Cancer Institute Board of Scientific Counselors, Dr. Witte has been continuously supported by the National Institutes of Health.

  • Wing┬áHung Wong

    Wing Hung Wong

    Stephen R. Pierce Family Goldman Sachs Professor of 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.

  • James Zou

    James Zou

    Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering

    Current Research and Scholarly InterestsMy 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.