Contact Information
suinlee AT cs.washington.edu
Prof. Su-In Lee, University of Washington, Seattle
Professor, Paul G. Allen School of Computer Science & Engineering
Adjunct Professor of Genome Sciences (GS), Electrical and Computer Engineering (ECE), and Biomedical Informatics and Medical Education (BIME)
Director, Computational Molecular Biology Program
AI Core Director, NIH Nathan Shock Center of Excellence in Basic Biology of Aging
Associate Director, Resuscitation Engineering Science Unit, UW Medicine
Education
PhD Electrical Engineering, Stanford University, Jan 2009
"Machine learning approaches to understand the genetic basis for complex traits" with Dr. Daphne Koller
MS Electrical Engineering, Stanford University, June 2003
BS Electrical Engineering and Computer Science, KAIST, Feb 2001
"Biologically inspired neural network approach using feature extraction and top-down selective attention for robust optical character recognition" with Prof. Soo-Young Lee
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.
Notable Services:
Associate Editor, Science Advances, American Association for the Advancement of Science (AAAS)
Standing Member, NIH Biodata Management and Analysis (BDMA) study section
Co-Founder/Co-Chair for Machine Learning in Computational Biology (MLCB)