Explainable Artificial Intelligence in Healthcare
Su-In Lee, PhD
Associate Professor, Paul G. Allen School of Computer Science & Engineering
Prof. Su-In Lee is a Paul G. Allen Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller in the Stanford Artificial Intelligence Laboratory. Before joining the UW in 2010, Lee was a Visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University School of Computer Science. She has received the National Science Foundation CAREER Award and been named an American Cancer Society Research Scholar. She has received generous grants from the National Institutes of Health, the National Science Foundation, and the American Cancer Society.
Her research aims to conceptually and fundamentally advance how AI can be integrated with biomedicine by addressing novel, forward-looking, and stimulating questions, enabled by unlocking the potential of AI. For example, although the primary focus of AI applications in the field of medicine had been on accurately predicting a patient’s phenotype or outcome, she focused on the question of why. This line of work has led to highly cited seminal publications in the field of foundational AI, clinical medicine, and computational molecular biology. Her recent research focuses on a broad spectrum of problems, including developing explainable AI (a.k.a. interpretable ML) techniques, identifying the cause and treatment of challenging diseases such as cancer and Alzheimer’s disease, and developing and auditing clinical AI models.