Emily Alsentzer is an Assistant Professor of Biomedical Data Science at Stanford who bridges machine learning, natural language processing, and clinical practice to make healthcare more equitable and actionable. With a decade of experience spanning a PhD from MIT/HST, postdoctoral work deploying ML at Mass General Brigham, and industry internships at Microsoft and Verily, she focuses on trustworthy models that perform well with limited labeled data and integrate heterogeneous clinical signals like EHRs and genomics. Her research emphasizes embedding biomedical knowledge from text and knowledge graphs into models so predictions can be both accurate and interpretable for clinicians. She has practical deployment experience in hospital systems and a track record of translating research into clinical workflows. Based in Palo Alto, she combines rigorous academic training with hands-on implementation, and her background includes leadership in student publishing and cross-disciplinary epidemiology projects that reveal a long-standing commitment to applied health impact.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD Health Science and Techonology - Medical Engineering and Medical Physics, Doctor of Philosophy - PhD Health Science and Techonology - Medical Engineering and Medical Physics at Massachusetts Institute of Technology
Valedictorian Distinguished Scholars Diploma, Valedictorian Distinguished Scholars Diploma at Hume-Fogg Academic High School
School for Science and Math at Vanderbilt
Master of Science (M.S.) Biomedical informatics, Master of Science (M.S.) Biomedical informatics at Stanford University
Contributions:1 release, 21 commits, 15 pushes in 1 month
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Emily Alsentzer - Assistant Professor at Stanford University