Dr. 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).
Genetic variation does not only underlie phenotypic diversity among individuals, but also documents the evolutionary history of a species. Research in our laboratory aims to uncover the evolutionary forces that have shaped the patterns of genetic variation in humans, to elucidate the genetics basis of complex traits, and to shed light on the mechanisms that lead to diverse phenotypes and disparate disease risk among populations. We approach these questions by developing statistical and computational approaches, by analyzing large-scale genomic data, and by collaborating with experts in a variety of fields.