School of Medicine
Showing 1-10 of 45 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. -
Jonathan H. Chen, MD, PhD
Assistant 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 Fries
Research Engineer, Med/BMIR
Current Role at StanfordI'm currently working as a staff research scientist in the Shah Lab and research scientist at Snorkel AI. My interests fall in the intersection of computer science and medical informatics. My research interests include:
• Foundation models and generative AI for healthcare
• Data-centric AI, focusing on training data curation, data generation, and quality assessment
• Learning with limited labeled data (e.g., weak supervision, zero/few-shot learning)
• Human-in-the-loop machine learning systems
