School of Medicine
Showing 1-20 of 35 Results
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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 Interests I 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
Associate Dean, School of Medicine, Professor of Medicine (Cardiovascular), of Genetics, of Biomedical Data Science and, by courtesy, of Pathology at the Stanford University Medical Center
Current Research and Scholarly Interests The 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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Carlos Bustamante
Professor of Biomedical Data Science, of Genetics and, by courtesy, of Biology
Current Research and Scholarly Interests My genetics research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. We focus on novel methods development for complex disease genetics and risk prediction in multi-ethnic settings. I am also interested in clinical data science and development of new diagnostics.I am also interested in disruptive innovation for healthcare including modeling long-term risk shifts and novel payment models.
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Helio Costa
Instructor, Biomedical Data Science
Bio Helio Costa, PhD, is a medical geneticist with expertise in oncology, medical genetics and genomics, computational biology, data science, software engineering, and product development. He is passionate about leveraging his interdisciplinary skillset to build and develop commercial-grade healthcare tools that aid in patient care and clinical decision support.
Dr. Costa's research focuses on developing, clinically validating, and implementing new medical diagnostic genetic tests and software for use at Stanford Health Care. His research group is also developing clinical algorithms using large-scale clinical laboratory datasets and patient electronic medical records to predict patient outcomes and aid in therapeutic clinical decision support.
He is a co-Investigator on the NIH-funded Clinical Genome Resource (ClinGen) Consortium, and leads the engineering and product management teams developing FDA-recognized medical software applications used by healthcare providers, researchers, and biotechnology companies to define the clinical relevance of genes and mutations identified in patients.
Dr. Costa is the founding director of the Stanford Clinical Data Science Fellowship where post-doctoral fellows engage in interdisciplinary clinical research and embed in health care workflows learning, building and deploying real-world health data solutions in the Stanford Health Care system. Additionally, he is an Attending Medical Geneticist, and Assistant Lab Director for the Molecular Genetic Pathology Laboratory at Stanford Health Care.
Dr. Costa received his BS in Genetics from University of California at Davis, his PhD in Genetics from Stanford University School of Medicine, and his ABMGG Clinical Molecular Genetics and Genomics fellowship training from Stanford University School of Medicine. -
Manisha Desai
Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly Interests Dr. 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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Bradley Efron
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science
Current Research and Scholarly Interests Research Interests:
BOOTSTRAP
BIOSTATISTICS
BAYESIAN STATISTICS -
Andrew Gentles
Assistant Professor (Research) of Medicine (Biomedical Informatics) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly Interests Computational systems biology of human disease. Particular focus on integration of high-throughput datasets with each other, and with phenotypic information and clinical outcomes.
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Olivier Gevaert
Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly Interests My 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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Trevor Hastie
John A. Overdeck Professor, Professor of Statistics and of Biomedical Data Sciences
Current Research and Scholarly Interests Flexible 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.
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Tina Hernandez-Boussard
Associate Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
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.
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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
Evidence-based medicine
Clinical and molecular epidemiology
Human genome epidemiology
Research design
Reporting of research
Empirical evaluation of bias in research
Randomized trials
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science -
Iain Johnstone
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.
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Teri Klein
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.
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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.
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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.
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Ying Lu
Professor of Biomedical Data Science and, by courtesy, of Radiology (Molecular Imaging) and of Epidemiology and Population Health
Current Research and Scholarly Interests Biostatistics, clinical trials, statistical evaluation of medical diagnostic tests, radiology, osteoporosis, meta-analysis, medical decisoin making
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Mark Musen
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.
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Richard A. Olshen
Professor of Biomedical Data Science, Emeritus
Current Research and Scholarly Interests My 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.
