School of Medicine
Showing 1-50 of 69 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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Imon Banerjee
Basic Life Science Research Scientist, Biomedical Data Science-Administration
Bio Imon Banerjee is currently working as a Research Scientist at the Biomedical Data Science Dept. Starting from 2016, she was a Post-doctoral scholar in the Laboratory of Quantitative Imaging at Stanford university. She received her Ph.D. from The University of Genova, Italy in 2016. During her Ph.D., she received Marie Curie European fellowship and worked as an early-stage researcher at The Institute for Applied Mathematics and Information Technologies, National Research Council, Italy. During her Ph.D., she developed novel techniques for building patient-specific 3D computational models. She completed her Master thesis in The European Organization for Nuclear Research (CERN), Geneva. Her research is focused on developing unstructured data analysis and big data mining techniques to support clinical diagnosis and treatment.
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Selen Bozkurt
Postdoctoral Research Fellow, Biomedical Data Sciences
Bio Selen Bozkurt is a postdoctoral scholar at Stanford University, Biomedical Data Science Department and Center for Biomedical Informatics Research. Her research area and interests have focused on health informatics research using electronic health records, machine learning and natural language processing. She also has work experience as a biostatistician in several projects. She is a member of RSNA Radiology Reporting Committee since 2009. Her PhD dissertation work was entitled "A Real Time Decision Support System for Mammography Interpretations" in which she developed an automated system for deep information extraction from mammography reports and an approach for real-time decision support driven by analysis of dictated radiology reports.
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Carlos Bustamante
Professor of Biomedical Data Science, of Genetics and, by courtesy, of Biology
Current Research and Scholarly Interests My research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. My group works on a variety of organisms and model systems ranging from humans and other primates to domesticated plant and animals. Much of our research is at the interface of computational biology, mathematical genetics, and evolutionary genomics.
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Michelle Whirl-Carrillo
Senior Research Scientist, Biomedical Data Science-Administration
Current Role at Stanford Associate Director, PharmGKB
www.pharmgkb.org -
Helio Costa
Instructor, Pathology
Bio Helio Costa, PhD, is a geneticist with expertise in genomics, molecular biology, oncology, and bioinformatics. He is currently an Instructor within the Departments of Pathology and Biomedical Data Science at Stanford Medical School. Dr. Costa's research utilizes next-generation sequencing to develop new genome and transcriptome profiling methods with the goal of translating these tools to clinical diagnostic tests for implementation at Stanford Health Care. He is also an Attending Geneticist, and Assistant Lab Director of the Molecular Genetic Pathology Laboratory for Stanford Health Care. Dr. Costa received his BS in Genetics from University of California, 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.
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Mark R. Cullen, MD
Director, Center for Population Health Sciences, Senior Associate Dean for Research, Professor of Medicine (Primary Care and Population Health), of Biomedical Data Science, of HRP (Epidemiology) and Senior Fellow at SIEPR
Current Research and Scholarly Interests Social and environmental determinants of health; role of workplace physical environment and work organization as causes of chronic disease and disability
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Francisco De La Vega
Adjunct Professor, Biomedical Data Science-Administration
Bio Prof. Francisco M. De La Vega is a geneticist and computational biologist with interests in cancer, population, and clinical genomics, and with extensive experience in the life sciences industry. He is a Distinguished Scientific Fellow and Vice President of Bioinformatics and at TOMA Biosciences, a privately held start-up company commercializing a technology for precision oncology derived from inventions at Stanford. Francisco is also Adjunct Professor in the Department of Biomedical Data science of the Stanford School of Medicine, a Director of the International Society of Computational Biology, and is or has been a member of the Steering Committee of the NIST-led Genome-in-a-Bottle consortium, the PanCancer Analysis of Whole Genomes project of the ICGC, and the Steering Committee of the 1000 Genomes Project. He has more recently contributed to start-up companies in the life sciences area in positions such as CSO (Annai Systems) and VP of Genomics (Real Time Genetics, Omicia). Previously, he spent over 13 yeas at Applied Biosystems (later Life Technologies and currently Thermo-Fisher), where he played a pivotal role in the development of several successful genetic analysis technologies. For this, he was inducted in 2009 to the Innovation & Invention Society of Life Technologies, a program that recognized the company’s most elite inventors, and in 2008 was a co-recipient of the Bio-IT World Best Practices Award in Basic Research.
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Manisha Desai
Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and, by courtesy, of Health Research and Policy
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 (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.
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John P.A. Ioannidis
C. F. Rehnborg Professor in Disease Prevention in the School, Professor of Medicine, of Health Research and Policy (Epidemiology) 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 and personalized medicine and health
Sociology of science -
Nilah Monnier Ioannidis
Postdoctoral Scholar, Biomedical Data Science-Administration
Bio Dr. Nilah Ioannidis is a postdoc in the Department of Biomedical Data Science working on statistical and computational methods for interpreting personal genomes. She develops machine learning tools to predict the clinical significance of rare variants of unknown significance from whole genome sequencing studies, as well as statistical methods to link personal genetic variation with personal transcriptome variation. During her PhD in Biophysics at Harvard University, she worked in the Department of Biological Engineering at M.I.T. and developed methods using hidden Markov modeling and Bayesian inference to analyze the dynamics of intracellular particles. She previously served as Research Director at the Jain Foundation, focused on the rare genetic disease dysferlinopathy, and held internships at the National Academy of Sciences and the journal Science.
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Aashish Jha
Postdoctoral Research Fellow, Biomedical Data Sciences
Bio I am interested in using genetic diversity to understand human demographic and evolutionary processes in human populations. In the past, I have worked in immunology, virology, and my PhD dissertation was in using experimentally evolved Drosophila melanogaster to understand genetic basis of complex traits.
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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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Arturo Lopez Pineda
Postdoctoral Research Fellow, Biomedical Data Sciences
Bio Arturo Lopez Pineda is a health data scientist in the Bustamante Lab at Stanford University. He is interested in the intersection between Artificial Intelligence and Medicine. Currently, he is working on the extraction of information from electronic medical records, its processing with natural language algorithms, and its use for case detection with machine learning modeling. He is interested in the development of affordable technologies to improve healthcare in Latin America.
Arturo is a member of Mexico's National System of Researchers (SNI) and a Fulbright Science and Technology alumnus. He holds a PhD and MS in Biomedical Informatics from the University of Pittsburgh School of Medicine, and a MS in Intelligent Systems and BS in Computer Science, both from Tecnologico de Monterrey (ITESM).
Arturo is member of the Global Oncology Young Leaders working group at Stanford. -
Ying Lu
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
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Mark Musen
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly Interests There are great opportunities for new discoveries and for ensuring the reproducibility of scientific results when experimental data—and descriptions of the methods used to generate and analyze those data—are available in public repositories. Our laboratory is studying the development of new methods to aid investigators in creating more comprehensive online descriptions both of their data and of their experiments that can be processed both by other scientists and by computers.
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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.
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Julia Palacios
Assistant Professor of Statistics and of Biomedical Data Science
Bio Dr. 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.
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Sylvia K. Plevritis, PhD
Professor of Radiology (General Radiology) and of Biomedical Data Science
Current Research and Scholarly Interests My 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.