Dr. Emily Alsentzer

Biography

Emily Alsentzer is an Assistant Professor in the Department of Biomedical Data Science at Stanford University. Her research focuses on augmenting clinical decision-making and broadening access to high-quality healthcare through the application of machine learning (ML) and natural language processing (NLP). She leverages heterogeneous clinical data, including electronic health records and genomic data, to provide actionable insights for clinicians, researchers, and patients. Alsentzer's work aims to design trustworthy machine learning methods that excel in settings with limited annotated data and safely integrate into clinical workflows. Previously, she served as a postdoctoral fellow at Brigham and Women’s Hospital, affiliated with Harvard Medical School. She earned her PhD in Health Science & Technology from MIT and Harvard, where she developed ClinicalBERT— a language model trained on electronic health records that has achieved millions of downloads on HuggingFace. Her contributions also include SHEPHERD, a graph neural network approach for diagnosing rare genetic diseases. Beyond her research, Alsentzer’s lab is actively recruiting students and postdoctoral researchers to advance trustworthy, deployable AI methods in healthcare.

Research Interests