School of Medicine
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Associate Professor (Research) of Anesthesiology, Perioperative and Pain Medicine (Adult MSD), of Pediatrics (Neonatology) and, by courtesy, of Biomedical Data Science
BioThank you for your interest. Please use the links on the bottom right side of this page to learn more about our laboratory's work.
Russ B. Altman
Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline), of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsI refer you to my web page for detailed list of interests, projects and publications. In addition to pressing the link here, you can search "Russ Altman" on http://www.google.com/
Euan A. Ashley
Associate Dean, School of Medicine, Roger and Joelle Burnell Professor of Genomics and Precision Health, Professor of Medicine (Cardiovascular Medicine), of Genetics, of Biomedical Data Science and, by courtesy, of Pathology
Current Research and Scholarly InterestsThe Ashley lab is focused on precision medicine. We develop methods for the interpretation of whole genome sequencing data to improve the diagnosis of genetic disease and to personalize the practice of medicine. At the wet bench, we take advantage of cell systems, transgenic models and microsurgical models of disease to prove causality in biological pathways and find targets for therapeutic development.
Professor (Research) of Biomedical Data Science (BDS), Emeritus
BioI received my PhD. in Mathematical Statistics in 1967. I joined the research community at the Stanford University School of Medicine, Division of Immunology & Rheumatology, in 1984 as head statistician directing the biostatistics consulting and analytic support of the Arthritis Rheumatism Aging Medical Information System (ARAMIS) and Multipurpose Arthritis Center (MAC) grant-related research programs. In 1993 I was appointed Associate Professor with a joint appointment in the Departments of Medicine and of Health Research & Policy, and am currently Professor of Biostatistics at Stanford University, emeritus since 2007. My contributions to the statistics literature span numerous fields, including methods of sample size estimation, efficiency and bias of estimators, research methods for kappa statistics, non-parametric classification methods and methods of assessing multi-parameter endpoints. I have over 200 peer-reviewed publications. I have been directly involved with the development of numerous criteria rules for classification of diseases and with establishing guidelines for clinical trial research and in proposing responder criteria for osteoarthritis drugs. Since 1987, I have been a consultant on an ad hoc basis to pharmaceutical and biotechnical firms, including both start-up and established companies. I have extensive experience with devices, drugs and biologics and have participated in all aspects of applying statistics to implement investigational plans; e.g.: for protocol development, design of trials, database design. I’ve been a member of the FDA Statistical Advisors Panel, the statistical member on numerous data safety monitoring boards, and frequently represent companies at meetings with the FDA
Professor of Medicine (Oncology), of Genetics and of Biomedical Data Science
Current Research and Scholarly InterestsThe Curtis laboratory for Cancer Computational and Systems Biology is focused on the development and application of innovative experimental, computational, and analytical approaches to improve the diagnosis, treatment, and early detection of cancer.
Manisha Desai (She/Her/Hers)
Kim and Ping Li Professor, Professor (Research) of Medicine (Quantitative Sciences Unit), of Biomedical Data Science and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsDr. Desai is the Director of the Quantitative Sciences Unit. She is interested in the application of biostatistical methods to all areas of medicine including oncology, nephrology, and endocrinology. She works on methods for the analysis of epidemiologic studies, clinical trials, and studies with missing observations.
Assistant Professor (Research) of Pathology, of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsComputational systems biology of human disease. Particular focus on integration of high-throughput datasets with each other, and with phenotypic information and clinical outcomes.
Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
John A. Overdeck Professor, Professor of Statistics and of Biomedical Data SciencesOn Leave from 01/01/2023 To 03/31/2023
Current Research and Scholarly InterestsFlexible statistical modeling for prediction and representation of data arising in biology, medicine, science or industry. Statistical and machine learning tools have gained importance over the years. Part of Hastie's work has been to bridge the gap between traditional statistical methodology and the achievements made in machine learning.
Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy 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.
Instructor, Biomedical Data Science
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioDr. Alexander Ioannidis is an Adjunct Professor in Computational and Mathematical Engineering, where he teaches machine learning and data science, and a researcher and Instructor in the Department of Biomedical Data Science. He earned his Ph.D. from Stanford University in Computational and Mathematical Engineering together with an M.S. in Management Science and Engineering (Optimization). Prior to Stanford, he worked in superconducting computing logic and quantum computing at Northrop Grumman. He graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil at the University of Cambridge from the Department of Applied Math and Theoretical Physics in Computational Biology, and a Diploma in Greek. As a current researcher in the Stanford School of Medicine, Department of Biomedical Data Science his work focuses on the design of algorithms and application of computational methods for problems in genomics, clinical data science, and precision health with a particular focus on underrepresented populations in Oceania and Latin America.
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 InterestsMeta-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 of Quantitative Science and Professor of Statistics and of Biomedical Data Sciences
Current Research and Scholarly InterestsEmpirical bias/shrinkage estimation; non-parametric, smoothing; statistical inverse problems.
Professor (Research) of Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics
Current Research and Scholarly InterestsCo-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 InterestsResearch 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.
Professor of Radiology (Thoracic Imaging), of Biomedical Informatics Research and of Biomedical Data Science
Current Research and Scholarly InterestsI am interested in the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. My laboratory is developing deep neural networks that detect and classify disease on medical images. We also develop natural language processing methods that use the narrative radiology report to create large annotated image training sets for supervised machine learning experiments.
Philip W. Lavori
Professor of Biomedical Data Science, Emeritus
Current Research and Scholarly InterestsBiostatistics, 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, by courtesy, of Biomedical Data ScienceOn Leave from 01/15/2023 To 05/15/2023
Current Research and Scholarly InterestsStatistics/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.