School of Medicine
Showing 1-46 of 46 Results
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Emily Alsentzer
Assistant Professor of Biomedical Data Science, of Medicine (Biomedical Informatics Research) and, by courtesy, of Computer Science
BioDr. Emily Alsentzer is an Assistant Professor in Biomedical Data Science and, by courtesy, Computer Science at Stanford University. Her research leverages machine learning (ML) and natural language processing (NLP) to augment clinical decision-making and broaden access to high quality healthcare. She focuses on integrating medical expertise into ML models to ensure responsible deployment in clinical workflows. Dr. Alsentzer completed a postdoctoral fellowship at Brigham and Women’s Hospital where she worked to deploy ML models within the Mass General Brigham healthcare system. She received her PhD from the Health Sciences and Technology program at MIT and Harvard Medical School and holds degrees in computer science (BS) and biomedical informatics (MS) from Stanford University. She has served as General Chair for the Machine Learning for Health Symposium and founding organizer for SAIL and the Conference on Health, Inference, and Learning (CHIL).
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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 Human-Centered AI 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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Alison Callahan
Research Engineer, Med/BMIR
BioAlison Callahan is an Instructor in the Center for Biomedical Informatics and Clinical Data Scientist in the Stanford Health Care Data Science team led by Nigam Shah. Her current research uses informatics to expand and improve the data available about pregnancy and birth, and to develop and maintain and EHR-derived obstetric database. She is also the co-leader of the OHDSI Perinatal & Reproductive Health (PRHeG) working group. Her work in the SHC Data Science team focuses on developing and implementing methods to assess and identify high value applications of machine learning in healthcare settings.
Alison completed her PhD in the Department of Biology at Carleton University in Ottawa, Canada. Her doctoral research focused on developing HyQue, a framework for representing and evaluating scientific hypotheses, and applying this framework to discover genes related to aging. She was also a developer for Bio2RDF, an open-source project to build and provide the largest network of Linked Data for the life sciences. Her postdoctoral work at Stanford applied methodologies developed during her PhD to study spinal cord injury in model organisms and humans in a collaboration with scientists at the University of Miami. -
Vivek Charu
Assistant Professor of Pathology and of Medicine (BMIR)
BioI am a physician and a biostatistician. My clinical expertise is in the diagnosis of non-neoplastic kidney and liver disease (including transplantation). My research interests center on the design of observational studies and clinical trials, the analysis of observational data, and causal inference.
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Jonathan H. Chen, MD, PhD
Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsInformatics solutions ares the only credible approach to systematically address challenges of escalating complexity in healthcare. Tapping into real-world clinical data streams like electronic medical records will reveal the community's latent knowledge in a reproducible form. Delivering this back as clinical decision support will uniquely close the loop on a continuously learning health system.
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Matthew A. Eisenberg
Clinical Assistant Professor (Affiliated), Med/BMIR
BioDr. Matthew A. Eisenberg joined Stanford Health Care in early 2013 and is the Medical Informatics Director for Analytics & Innovation with a focus on interoperability and health information exchange, regulatory reporting, health care analytics, patient reported outcomes and other uses of technology to meet our strategic initiatives.
Dr. Eisenberg is board certified in Pediatrics and Clinical Informatics. He is a Clinical Assistant Professor (Affiliated) in the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine and he serves as the Stanford Health Care site director for the Stanford Clinical Informatics Fellowship Program. He previously held the position of Clinical Assistant Professor in Pediatrics at the University of Washington School of Medicine. He is a current member of the eHealth Exchange Coordinating Committee, a Sequoia Project Board member and serves as the current chair of the Epic Care Everywhere Network Governing Council. He is a member of the Carequality Advisory Council (past co-chair) and a member of IHE USA Implementation Committee. He is a Fellow of the American Academy of Pediatrics and a member of the American Medical Informatics Association and their Clinical Informatics Community. -
Jason Alan Fries
Assistant Professor of Biomedical Data Science and of Medicine (BMIR)
BioJason Fries' research focuses on training and evaluating foundation models for healthcare, positioned at the intersection of computer science, medical informatics, and hospital systems. His work explores the use of electronic health record (EHR) data to contextualize human health, leveraging longitudinal patient information to inform model development and evaluation. His research has been published in venues such as NeurIPS, ICLR, AAAI, Nature Communications, Nature Medicine and npj Digital Medicine.
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Andrew Gentles
Associate 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
On Partial Leave from 12/01/2025 To 02/28/2026Current 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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Summer Han
Associate Professor (Research) of Neurosurgery, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy current research focuses on understanding the genetic and environmental etiology of complex disease and developing and evaluating efficient screening strategies based on etiological understanding. The areas of my research interests include statistical genetics, molecular epidemiology, cancer screening, health policy modeling, and risk prediction modeling. I have developed various statistical methods to analyze high-dimensional data to identify genetic and environmental risk factors and their interactions for complex disease.
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Josef Hardi
Software Dvlpr 3, Med/BMIR
BioI'm a software engineer with over 15 years of experience building reliable, scalable software systems. I’m especially passionate about software engineering, data modeling, and the emerging potential of agentic large language models (LLMs).
I work at the Stanford Center for Biomedical Informatics Research, where I help develop Protégé and WebProtégé, which are tools used worldwide for creating and managing ontologies. Recently, I contributed to the Human BioMolecular Atlas Program (HuBMAP) project, where I helped build the Human Reference Atlas (HRA) knowledge graph and designed metadata schemas to support a range of assay datasets produced by the consortium.
My core technical strengths are in Java, JavaScript, and Python. I enjoy working at the intersection of software engineering and data to build tools that empower researchers and improve data interoperability. -
Zihuai He
Associate Professor (Research) of Neurology and Neurological Sciences (Neurology Research), of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsStatistical genetics and other omics to study Alzheimer's disease and aging.
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Tina Hernandez-Boussard
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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Daniel Katz
Assistant Professor of Medicine (BMIR)
BioDaniel Katz is an Assistant Professor of Medicine in the Stanford Center for Biomedical Informatics Research (BMIR) and the Cardiovascular Medicine Divisions. He practices as an Advanced Heart Failure and Transplant Cardiologist. He completed internal medicine residency at Massachusetts General Hospital, general cardiology training at Beth Israel Deaconess Medical Center, and then joined Stanford in 2021 for his advanced heart failure training. His research focuses on identifying the various pathophysiologic patterns and mechanisms that lead to the heterogeneous syndrome of heart failure. His efforts leverage high dimensional data in many forms including clinical phenotypes, plasma proteomics, metabolomics, and genetics. He is presently engaged in analysis of multi-omic data from the Molecular Transducers of Physical Activity Consortium (MoTrPAC) and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. His clinical interests include advanced heart failure, transplant cardiology, and mechanical circulatory support.
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Christian Kindermann
Research Engineer, Med/BMIR
Current Role at StanfordI am a research engineer specializing in semantic technologies (ontologies and knowledge graphs). My work focuses on helping life-science practitioners and researchers manage their data.
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Teri Klein
Professor (Research) of Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics
On Partial Leave from 01/16/2026 To 12/18/2026Current Research and Scholarly InterestsCo-founder, Pacific Symposium on Biocomputing
NIEHS, Site Visit Reviewer
NIH, Study Section Reviewer -
Curtis Langlotz
Senior Associate Vice Provost for Research, Professor of Radiology (Integrative Biomedical Imaging Informatics), of Medicine (BMIR), of Biomedical Data Science and Senior Fellow at the Stanford Institute for Human-Centered AI
Current Research and Scholarly InterestsMy laboratory develops machine learning methods to help physicians detect disease and eliminate diagnostic errors. My laboratory is developing neural network systems that detect and classify disease on medical images. We also develop natural language processing methods that use the narrative radiology report for contrastive learning and other multi-modal methods that improve the accuracy and capability of machine learning systems.
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Maya Mathur
Associate Professor (Research) of Pediatrics, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsSynthesizing evidence across studies while accounting for biases
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Mark Musen
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.
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Martin O'Connor
Software Dvlpr 3, Med/BMIR
Current Role at StanfordResearch software developer at Stanford Center for Biomedical Informatics Research (BMIR)
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Natalie Pageler
Clinical Professor, Clinical Informatics
Clinical Professor, Medicine - Biomedical Informatics ResearchCurrent Research and Scholarly InterestsIn my administrative role, I oversee the development and maintenance of clinical decision support tools within the electronic medical record. These clinical decision support tools are designed to enhance patient safety, efficiency, and quality of care. My research focuses on rigorously evaluating--1) how these tools affect clinician knowledge, attitudes, and behaviors; and 2) how these tools affect clinical outcomes and efficiency of health care delivery.
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Shriti Raj
Assistant Professor of Medicine (Center for Biomedical Informatics Research)
BioShriti is an Assistant Research Professor in Stanford’s Center for Biomedical Informatics Research and a Junior Faculty Fellow at the Institute for Human-Centered AI. Her research focuses on developing and evaluating human-centered decision-support techniques to help patients and clinicians make health data and algorithms actionable. She is particularly interested in creating tools to support the use of wearable health data and studying their impact on chronic condition management.
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Nigam H. Shah, MBBS, PhD
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsWe answer clinical questions using aggregate patient data at the bedside. The Informatics Consult Service (https://greenbutton.stanford.edu/) put this idea in action and led to the creation of Atropos Health. We build predictive models that allow taking mitigating actions, keeping the human in the loop.
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Walter Sujansky
Adjunct Professor, Department of Medicine, Center for Biomedical Informatics Research
BioWalter Sujansky, MD PhD is an Adjunct Professor of Biomedical Informatics at the Stanford Center for Biomedical Informatics Research in the Stanford Department of Medicine. Dr. Sujansky co-teaches BMI-210 Modeling Biomedical Systems, where he lectures on a variety of topics, including deep neural networks, probabilistic reasoning, electronic health records, and health data integration and interoperability. He also advises students in the Biomedical Data Science Graduate Program, an interdisciplinary graduate and postdoctoral training program that is part of the Department of Biomedical Data Science. His research interests include the modeling of biomedical concepts based on formal logic and the engineering of features for biomedical machine learning algorithms.
Dr. Sujansky earned an M.D. and a Ph.D. in Medical Informatics from Stanford University, where his doctoral research addressed heterogeneous database integration and clinical decision support. He also earned a B.A. in Economics from Harvard University.
Dr. Sujansky is also the managing consultant at Sujansky & Associates, LLC, a consulting firm that specializes in the representation, management, and analysis of clinical data in information systems. In this capacity, his work focuses on the modeling of complex biomedical data related to patient phenotyping, clinical genomics, quality measurement, automated decision support, and machine learning. His firm has helped to develop shared computing resources such as the California Joint Replacement Registry and the Laboratory Interoperability Data Repository. The firm's clients include the federal and state governments, non-profit organizations, health information system developers, and drug/device manufacturers. Dr. Sujansky also provides forensic analysis of health information technologies for medical malpractice and intellectual property litigation. -
Michael Wornow
Affiliate, Med/BMIR
BioMichael is a computer science PhD student focused on developing and operationalizing large-scale pretrained models ("foundation models") in healthcare. He is advised by Nigam Shah and Chris Re and is supported by an NSF Graduate Research Fellowship.
