School of Medicine
Showing 1-50 of 164 Results
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Russ B. Altman
Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine, of Biomedical Data Science, Senior Fellow at the Stanford Institute for HAI and Professor, 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/
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Euan A. Ashley
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.
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Shaimaa Bakr
Masters Student in Biomedical Data Science, admitted Autumn 2020
BioShaimaa is a graduate of the Ph.D. program, the Department of Electrical Engineering at Stanford and currently a postdoctoral researcher at the Gevaert lab at the Stanford Center for Biomedical Informatics Research (BMIR). Shaimaa is interested in developing multi-modal deep learning models using biomedical data with focus on genomic, radiology and histopathology data and applying these models to solve problems in cancer and other diseases. Prior to Stanford, she received her B.Sc. (Summa Cum Laude) from the American University in Cairo, where she studied Electronics Engineering and Computer Science. She obtained her MS degree in Electrical Engineering from Rensselaer Polytechnic Institute, working in the Cognitive and Immersive Systems lab, and advised by Professor Richard Radke.
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Corinne Beck
Program Manager, Center for Cancer Systems Biology (CCSB), Biomedical Data Science
Current Role at StanfordProgram Manager
Stanford Center for Cancer Systems Biology (CCSB)
Plevritis Lab
Department of Biomedical Data Science (DBDS)
School of Medicine | Stanford University -
Daniel Bloch
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
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Michelle Whirl-Carrillo
Principal Investigator and Director, PharmGKB, Biomedical Data Science
Current Role at StanfordPrincipal Investigator and Director, PharmGKB
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Christina Curtis
RZ Cao Professor, Professor 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.
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Francisco M. De La Vega
Adjunct Professor, Biomedical Data Science
BioFrancisco De La Vega is a distinguished geneticist and computational biologist, and an experienced technical executive, widely recognized for his expertise in clinical and population genomics, and bioinformatics. Currently serving as the Vice President of Hereditary Disease at Tempus Labs, Francisco is spearheading the development of comprehensive germline genetic tests and conducting innovative research into racial disparities in cancer leveraging Tempus’ multimodal Real-World Data. His work focuses on uncovering the connections between genetic ancestry and cancer genome mutational profiles that may help explain the differences in cancer incidence and outcomes across races and ethnicities. In addition to his role at Tempus Labs, Francisco is an Adjunct Professor in the Department of Biomedical Data Science at Stanford University School of Medicine and is a member of the Board of Directors of the International Society of Computational Biology, serving from 2022 to 2025.
Francisco teaches BIODS-235: "Best practices for developing data science software for clinical and healthcare applications" every Winter quarter. -
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.
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Andrew Gentles
Assistant Professor (Research) of Pathology, of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsComputational systems biology
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Olivier Gevaert
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.
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Li Gong
Scientific Data Curator 3, Biomedical Data Science
Current Role at StanfordI am a senior scientific curator at PharmGKB, and also serves as the program manager for the Stanford ClinGen team and coordinator for the ClinGen Pharmacogenomics Working Group.
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Ryunosuke (Ryan) Goto
Ph.D. Student in Biomedical Data Science, admitted Autumn 2024
BioRyunosuke (Ryan) Goto is a PhD student in Biomedical Data Science and a Knight-Hennessy Scholar. Prior to Stanford, Ryan was a Chief Resident in Pediatrics at Nagano Children's Hospital and the University of Tokyo Hospital. He is interested in developing and applying statistical tools to investigate human disease and advance precision medicine. Ryan’s work has been published in The Lancet, JAMA Pediatrics, and Pediatrics, among other journals.
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Trevor Hastie
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.
