Contact Information
suinlee AT cs.washington.edu
Prof. Su-In Lee, University of Washington, Seattle
Paul G. Allen Professor, 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, AI Technology for Health (AI4health)
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 Prof. Daphne Koller in Stanford AI Lab
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
Seoul Science High School, Feb 1997
Bio:
Prof. Su-In Lee, the Paul G. Allen Professor of Computer Science at UW, earned her PhD from Stanford University in 2009 under the mentorship of Prof. Daphne Koller. She joined UW in 2010 after serving as a visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University School of Computer Science. Recognized for her groundbreaking contributions to AI, biology, and medicine, Prof. Lee has received prestigious accolades including the National Science Foundation (NSF) CAREER Award, the International Society for Computational Biology (ISCB) Innovator Award, and the Samsung Ho-Am Prize, often referred to as the "Korean Nobel Prize," and designation as an American Cancer Society (ACS) Research Scholar and a Fellow of American Institute for Medical and Biological Engineering (AIMBE). Notably, she is recognized as a pioneer and trailblazer in explainable AI (XAI), significantly enhancing ML model interpretability.
Prof. Lee's recent contributions revolve around essential XAI principles and techniques, including her groundbreaking SHAP framework. Her innovative biomedical research spans basic biology to clinical medicine, enabled by XAI advancements. Conceptually advancing the integration of AI with biomedicine, her work addresses forward-looking scientific questions, enabling novel discoveries from high-throughput molecular data and electronic health records and advancing healthcare. This pioneering line of work has led to highly cited publications across foundational AI, computational molecular biology, and clinical medicine.