Full Name
Su-In Lee
Job title
Paul G. Allen Professor
University/company
Allen School of Computer Science & Engineering, University of Washington
Speaker bio
Prof. Su-In Lee is a Paul G. Allen Professor of Computer Science & Engineering at the UW. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller in the Stanford Artificial Intelligence Laboratory in Computer Science. 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 numerous generous grants from the National Institutes of Health (NIH), the National Science Foundation (NSF), and the American Cancer Society.
Prof. Lee's research has conceptually and fundamentally advanced the way AI is integrated with biomedicine by addressing novel, forward-looking, and stimulating scientific questions, enabled by AI advances. For example, when the primary focus of AI applications in biomedicine was on making accurate predictions using machine learning (ML) models, she uniquely focused on why a certain prediction was made by developing novel AI theories, principles, and techniques to improve the interpretability of ML models, which are applicable to a broad spectrum of problems beyond biomedicine. This line of work has led to highly cited seminal publications in the field of foundational AI, clinical medicine, and computational molecular biology. Her research aims to push the boundaries of both foundational AI and biomedicine, to address new questions, and make novel discoveries from high-throughput molecular data and electronic medical record data.
Prof. Lee's research has conceptually and fundamentally advanced the way AI is integrated with biomedicine by addressing novel, forward-looking, and stimulating scientific questions, enabled by AI advances. For example, when the primary focus of AI applications in biomedicine was on making accurate predictions using machine learning (ML) models, she uniquely focused on why a certain prediction was made by developing novel AI theories, principles, and techniques to improve the interpretability of ML models, which are applicable to a broad spectrum of problems beyond biomedicine. This line of work has led to highly cited seminal publications in the field of foundational AI, clinical medicine, and computational molecular biology. Her research aims to push the boundaries of both foundational AI and biomedicine, to address new questions, and make novel discoveries from high-throughput molecular data and electronic medical record data.
Speaking At
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