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.
Roxana Daneshjou, MD, PhD
Instructor, Biomedical Data Science
BioI am interested in bridging new technologies such as genomics and machine learning with clinical medicine. I am also interested in the use of Twitter for scientific communication and medical education. I am on Twitter: @RoxanaDaneshjou.
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 Sciences
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). 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. 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
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 Science
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.
Professor of Biomedical Data Science and, by courtesy, of Epidemiology
Current Research and Scholarly InterestsBiostatistics, clinical trials, statistical evaluation of medical diagnostic tests, radiology, osteoporosis, meta-analysis, medical decision making
Stephen B. Montgomery
Associate Professor of Pathology, of Genetics, of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsWe focus on understanding the effects of genome variation on cellular phenotypes and cellular modeling of disease through genomic approaches such as next generation RNA sequencing in combination with developing and utilizing state-of-the-art bioinformatics and statistical genetics approaches. See our website at http://montgomerylab.stanford.edu/
Stanford Medicine Professor of Biomedical Informatics Research, Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsModern science requires that experimental data—and descriptions of the methods used to generate and analyze the data—are available online. Our laboratory studies methods for creating comprehensive, machine-actionable descriptions both of data and of experiments that can be processed by other scientists and by computers. We are also working to "clean up" legacy data and metadata to improve adherence to standards and to facilitate open science broadly.
Richard A. Olshen
Professor of Biomedical Data Science, Emeritus
Current Research and Scholarly InterestsMy research is in statistics and their applications to medicine and biology. Many efforts have concerned tree-structured algorithms for classification, regression, survival analysis, and clustering.
Associate Professor of Statistics and of Biomedical Data Science
BioDr. Palacios seek to provide statistically rigorous answers to concrete, data driven questions in evolutionary genetics and public health . My research involves probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes, Bayesian nonparametrics and recent developments in machine learning and statistical theory for big data.
Sylvia K. Plevritis, PhD
Professor of Biomedical Data Science and of Radiology (Integrative Biomedical Imaging Informatics at Stanford)
Current Research and Scholarly InterestsMy research program focuses on computational modeling of cancer biology and cancer outcomes. My laboratory develops stochastic models of the natural history of cancer based on clinical research data. We estimate population-level outcomes under differing screening and treatment interventions. We also analyze genomic and proteomic cancer data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.
Professor of Biomedical Data Science, of Radiology (Integrative Biomedical Imaging Informatics at Stanford), of Medicine (Biomedical Informatics Research) and, by courtesy, of Ophthalmology
Current Research and Scholarly InterestsMy research interest is imaging informatics--ways computers can work with images to leverage their rich information content and to help physicians use images to guide personalized care. Work in our lab thus lies at the intersection of biomedical informatics and imaging science.
Assistant Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Urology and of Biomedical Data Science
Current Research and Scholarly InterestsDr. Mirabela Rusu focuses on developing analytic methods for biomedical data integration, with a particular interest in radiology-pathology fusion. Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization.
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsStatistical models and reasoning are key to our understanding of the genetic basis of human traits. Modern high-throughput technology presents us with new opportunities and challenges. We develop statistical approaches for high dimensional data in the attempt of improving our understanding of the molecular basis of health related traits.
Associate Professor of Biomedical Data Science, of Biochemistry and, by courtesy, of Statistics and of Biology
Current Research and Scholarly Interestsstatistical computational biology focusing on splicing, cancer and microbes
Nigam H. Shah, MBBS, PhD
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsWe analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system.
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.
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.
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
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 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.
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.