School of Medicine
Showing 1-10 of 78 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 HAI 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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Vasiliki (Vicky) Bikia
Postdoctoral Scholar, Biomedical Informatics
BioDr. Vasiliki Bikia is a Fellow at the Institute for Human-Centered Artificial Intelligence and Postdoctoral Scholar at Stanford University, working with Prof. Roxana Daneshjou. She received her Advanced Diploma degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTH), Greece, in 2017, and her Ph.D. degree in Biomedical Engineering from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland, in 2021. Her Ph.D. research addressed the clinical need for providing non-invasive tools for cardiovascular monitoring leveraging machine learning and physics-based numerical modeling.
Her current work focuses on developing large multimodal models to enhance biomarker identification and patient outcome prediction. At Stanford, she has also contributed to the Stanford Spezi framework, designing and prototyping the Spezi Data Pipeline tool for enhanced digital health data accessibility and analysis workflows. Her research interests include health algorithms, clinical and digital biomarkers, machine learning, non-invasive monitoring, and the application of large language models for personalized healthcare, predictive analytics, and enhancing patient-clinician interactions. -
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.
