School of Medicine
Showing 11-20 of 36 Results
Associate Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and of Surgery
Current Research and Scholarly Interests My background and expertise is in the field of computational biology, with concentration in health services research. A key focus of my research is to apply novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. My research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables us to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.
John P.A. Ioannidis
Professor of Medicine (Stanford Prevention Research), of Epidemiology and Population Health and by courtesy, of Statistics and of Biomedical Data Science
Current Research and Scholarly Interests Meta-research
Clinical and molecular epidemiology
Human genome epidemiology
Reporting of research
Empirical evaluation of bias in research
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science
Marjorie Mhoon Fair Professor in Quantitative Science and Professor of Statistics and of Biomedical Data Sciences
Current Research and Scholarly Interests Empirical bias/shrinkage estimation; non-parametric, smoothing; statistical inverse problems.
Professor (Research) of Biomedical Data Science and of Medicine (BMIR)
Current Research and Scholarly Interests Co-founder, Pacific Symposium on Biocomputing
NIEHS, Site Visit Reviewer
NIH, Study Section Reviewer
Tze Leung Lai
Ray Lyman Wilbur Professor and Professor, by courtesy, of Biomedical Data Science
Current Research and Scholarly Interests Research interests include clinical trial design, cancer biostatistics, survival analysis, adaptation and sequential experimentation, change-point detection and segmentation, stochastic optimization, time series and inference on stochastic processes, hidden Markov models and genomic applications.
Philip W. Lavori
Professor of Biomedical Data Science, Emeritus
Current Research and Scholarly Interests Biostatistics, clinical trials, longitudinal studies, casual inference from observational studies, genetic tissue banking, informed consent. Trial designs for dynamic (adaptive) treatment regimes, psychiatric research, cancer.
Laura C. Lazzeroni, Ph.D.
Professor (Research) of Psychiatry and Behavioral Sciences and of Biomedical Data Science
Current Research and Scholarly Interests Statistics/Data Science. I develop & apply models, methods & algorithms for complex data in medical science & biology. I am also interested in the interplay between fundamental statistical properties (e.g. variability, bias, p-values) & how scientists actually use & interpret data. My work in statistical genetics includes: the invention of Plaid bi-clustering for gene expression data; methods for twin, association, & family studies; multiple testing & estimation for high dimensional arrays.
Professor of Biomedical Data Science and, by courtesy, of Radiology (Molecular Imaging) and of Health Research and Policy (Epidemiology)
Current Research and Scholarly Interests Biostatistics, clinical trials, statistical evaluation of medical diagnostic tests, radiology, osteoporosis, meta-analysis, medical decisoin making
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly Interests It's important to ensuring that experimental data?and descriptions of the methods used to generate and analyze the data?are available online. Our laboratory studies methods for creating more comprehensive metadata descriptions both of data and of experiments that can be processed both by other scientists and by computers. We are also working to clean up legacy data and metadata to facilitate open science broadly. Other work focuses on management of knowledge using knowledge graphs.