School of Medicine
Showing 11-20 of 46 Results
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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.
