School of Medicine
Showing 1-85 of 85 Results
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Minhaj Nur Alam
Postdoctoral Research Fellow, Biomedical Data Sciences
BioI am a Postdoctoral Scientist at the Stanford Department of Biomedical Data Sciences, with a research focus on Medical AI/ML applications and quantitative image processing (Ophthalmology and Radiology). I have extensive experience in quantitative image biomarker development and incorporating machine learning algorithms for computer aided diagnosis/classification in Ophthalmology and Radiology. I hold a PhD in Bioengineering (CV/AI applications in Ophthalmology) from University of Illinois at Chicago.
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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
On Partial Leave from 01/01/2021 To 06/30/2021Current 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
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 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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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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Carlos Bustamante
Professor of Biomedical Data Science, of Genetics and, by courtesy, of Biology
Current Research and Scholarly InterestsMy 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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Michelle Whirl-Carrillo
Senior Research Scientist, Biomedical Data Science
Current Role at StanfordPharmGKB Director
www.pharmgkb.org -
Helio Costa
Instructor, Biomedical Data Science
BioHelio 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. -
Roxana Daneshjou
Clinical Scholar, Dermatology
Postdoctoral Research Fellow, Biomedical Data SciencesBioI 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.
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Francisco M. De La Vega
Adjunct Professor, Biomedical Data Science
BioProf. Francisco De La Vega is a geneticist, computational biologist and experienced technical executive of the life sciences industry, having spent over a decade at Applied Biosystems/Life Technologies developing several successful genetic analysis products, and more recently contributing to technology start-up companies focused on bringing genome sequencing into the clinic. He has participated in several breakthrough international projects such as the 1000 Genomes Project, the Genome-in-a-Bottle Consortium, and the International Cancer Genome Consortium. Francisco has co-authored more than 100 scientific publications, including papers in top journals such as Nature, Nature Genetics, Science, Genome Research and others, which have received over 20,000 citations. Currently he is Chief Scientific Officer and Senior Vice President of Research and Development at Fabric Genomics, an Oakland-based privately held company that develops an Artificial Intelligence-driven software-as- a-service platform for genomic interpretation and clinical reporting from genomes, exomes, and gene panels.
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Manisha Desai
Professor (Research) of Medicine (Biomedical Informatics), 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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Florian Dubost
Postdoctoral Research Fellow, Biomedical Data Sciences
BioMy research objectives are focused on the development of artificial intelligence technologies for neurology research. My graduate training revolved around medical engineering and offered me a multidisciplinary advanced education in computer science, physics, mathematics, biology, and chemistry. As I was progressing towards the start of my PhD, I decided to develop my expertise in machine learning— a type of artificial intelligence—and neurology, working for example on the automatic classification of fMRI signals of the auditory cortex under the supervision of Dr. Takerkart during my studies in Centrale Marseille, France. In Germany, I strengthened my expertise in machine learning in Prof. Navab's chair and developed and published an automated method for the segmentation of medical images based on Markov Chain Monte Carlo. During my PhD in the Netherlands, I focused on deep learning and neurology and developed methods for weakly supervised learning, regression neural networks, and brain lesion detection and quantification from MRI. One of my major contribution is my work on the automated quantification and detection of enlarged perivascular spaces—a type of brain lesion related to cerebral small vessel disease. During my PhD, I visited Prof. Rost group at MGH, Harvard Medical School, to strengthen my expertise in neurology research, and developed and published deep learning registration methods for clinical brain MRI. I am now doing my postdoctoral training in Prof. Daniel Rubin's group at Stanford with the additional supervision of the neurologist Prof. Lee-Messer. I am developing deep learning methods to detect and predict seizures from EEG and video recordings of epileptic patients.
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Bradley Efron
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science
Current Research and Scholarly InterestsResearch 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 InterestsComputational 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 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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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.
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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 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.
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Dr. Alexander Ioannidis
Postdoctoral Research Fellow, Biomedical Data Sciences
BioDr. Alexander Ioannidis (PhD, MPhil) graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil in Computational Biology and Diploma in Greek from the University of Cambridge. His Ph.D. from Stanford University was in Computational and Mathematical Engineering, where he still teaches machine learning and data science as an Adjunct Lecturer in the School of Engineering. He also has an M.S. in Mgmt. Sci. and Eng. (Optimization) from Stanford. Prior to Stanford, he worked in superconducting computing logic and quantum computing at Northrop Grumman. As a current research fellow in the Stanford School of Medicine (Department of Biomedical Data Science), his work focuses on applying computational methods to problems in genomics and population genetics.
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I work on novel algorithm design (particularly ancestry related) for several large-scale genomic studies that aim at understanding genetic causes of disease.
I also focus on projects at the intersection of history and population genetics, including work with native communities. As the grandson of Cappadocian refugees expelled from their native land, I try to engage with the complex sentiments of displaced indigenous peoples in these projects. Pain over the disruption of community heritage and over dispossession from traditional sites often remains raw. If engagement with descendant communities is lacking, research into our past can often feel like a continuation, even a legitimation, of dispossession. Combined alongside a dialogue with native communities, however, genetics can play a small role in helping to reclaim ancestral stories and dispersed ancestral connections. I hope our work in this area plays a constructive role in that process.
As written by the poet Rumi in the language of the Cappadocians (Rûm),
پیمی تیِ پَاثیِسْ پیمی تی خاسِس
“Tell me what happened to you, tell me what you have lost.”
[Rumi; Konya ms 67; translit. πε με τι έπαθες, πε με τι έχασες] -
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
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 InterestsEmpirical 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 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.
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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.
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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 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.
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Binglan Li
Postdoctoral Research Fellow, Biomedical Data Sciences
BioMy research interests primarily lie in two parts: 1) understanding genetic architecture of complex diseases and traits, and 2) clinical implementation of human genetics discoveries, for example, pharmacogenomics. I received my Ph.D. degree in Genomics and Computational Biology from University of Pennsylvania. My dissertation focused on identifying complex trait or disease-associated genes via genomic regulation-informed gene-based analyses. I am now a postdoctoral fellow in the Klein Lab (PharmGKB group). I am currently working on the Pharmacogenomics Clinical Annotation Tool (PharmCAT), a one-stop bioinformatics tool that analyzes pharmacogenomics variants from genotypic datasets and generates reports with genotype-based prescribing recommendations to supports clinical pharmacogenomics implementations and treatment decisions.
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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 InterestsBiostatistics, clinical trials, statistical evaluation of medical diagnostic tests, radiology, osteoporosis, meta-analysis, medical decisoin making
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Daniel Mas Montserrat
Postdoctoral Research Fellow, Biomedical Data Sciences
BioDaniel Mas Montserrat holds a PhD in Electrical and Computer Engineering from Purdue University. Previously he graduated summa cum laude from the Polytechnic University of Catalonia in Audiovisual Systems in Telecommunications Engineering. Currently, he is a research fellow at the Stanford School of Medicine (Department of Biomedical Data Science). His research focuses on applying computational methods to problems in population genetics and biomedicine.
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Mark Musen
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
On Partial Leave from 04/01/2021 To 09/30/2021Current Research and Scholarly InterestsIt'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 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.
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Julia Palacios
Assistant Professor of Statistics and of Biomedical Data Science
On Partial Leave from 09/01/2020 To 08/31/2021BioDr. 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 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.
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Johannes Reiter
Assistant Professor of Radiology (Canary Center) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsMy research focuses on the stochastic biological processes underlying cancer evolution with the goal to improve diagnosis, prognosis, and treatment of tumors. I develop computational methods and design mathematical models to generate novel hypotheses and explain observations on a mechanistic level.
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David Rogosa
Associate Professor of Education and, by courtesy, of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsStatistical issues in educational assessment; analysis of longitudinal data.
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Daniel Rubin
Professor of Biomedical Data Science and of Radiology (Integrative Biomedical Imaging Informatics at Stanford), of Medicine (Biomedical Informatics Research) and, by courtesy, of Ophthalmology and of Computer Science
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.
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Chiara Sabatti
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.
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Julia Salzman
Associate Professor of Biochemistry and of Biomedical Data Science
Current Research and Scholarly InterestsCircular RNA regulation and function; computational and experimental approaches
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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.
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Laurel Stell
Research Engineer, Biomedical Data Science
Current Role at StanfordPrimarily collaborating with VA researchers on GWAS using MVP data. Also providing statistical support to some Stanford clinicians conducting investigational research.
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Suzanne Tamang
Instructor, Biomedical Data Science
BioSuzanne Tamang is based at the Center for Population Health Sciences She received her Ph.D. in Computer Science from the City University of New York and completed her postdoctoral training at the Stanford's Center for Biomedical Bioinformatics.
At Stanford, Suzanne's collaborations span the Alcoa Research Consortium, the Clinical Excellence Research Center and the Stanford Cancer Institute. She is also affiliated with the Department of Rheumatology at UCSF. -
John S. Tamaresis, PhD, MS
Biostatistician, Biomedical Data Science
BioDr. Tamaresis joined the Stanford University School of Medicine in Summer 2012. He earned the Ph.D. in Applied Mathematics from the University of California, Davis and received the M.S. in Statistics from the California State University, East Bay. He has conducted research in computational biology as a postdoctoral scholar at the University of California, Merced and as a biostatistician at the University of California, San Francisco.
As a statistician, Dr. Tamaresis has developed and validated a highly accurate statistical biomarker classifier for gynecologic disease by applying multivariate techniques to a large genomic data set. His statistical consultations have produced data analyses for published research studies and analysis plans for novel research proposals in grant applications. As an applied mathematician, Dr. Tamaresis has created computational biology models and devised numerical methods for their solution. He devised a probabilistic model to study how the number of binding sites on a novel therapeutic molecule affected contact time with cancer cells to advise medical researchers about its design. For his doctoral dissertation, he created and analyzed the first mathematical system model for a mechanosensory network in vascular endothelial cells to investigate the initial stage of atherosclerotic disease. -
Lu Tian
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. -
Robert Tibshirani
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.
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Dennis Wall
Associate 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
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Wing Hung Wong
Stephen R. Pierce Family Goldman Sachs Professor in 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.
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James Zou
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.
