With a Ph.D. in artificial intelligence and computer vision and postgraduate training in biomedical imaging & computational neuroscience, I solve critical problems in healthcare and neuroscience.
My research group focuses on developing Translational Artificial Intelligence (AI) algorithms in medicine and mental health. My work involves the automatic analysis of human activities and behaviors from videos, connecting how humans perform actions to the brain by analyzing magnetic resonance images (MRIs). By exploring explainable machine learning algorithms, I aim to uncover the underlying factors of neurodegenerative and neuropsychiatric diseases and their impact on everyday life.
My research concentrates on and connects two main areas: digital humans and human neuroscience. I analyze 3D motion, actions, and behaviors using various human sensing technologies, such as video and sensory data. Additionally, I employ clinical and cognitive tests, as well as neuroimaging modalities like MRIs, to explore brain function and neural processes. Integrating these technologies to enhance clinical applications and provide deeper insights into the complexities of human behavior and brain function, my group develops world models for neuroscience.
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).
Assistant Professor of Neurology, Medicine (BMIR/QSU) and, by courtesy, Biomedical Data Science