Professor Su-In Lee is the Boeing Endowed Professor of Computer Science at the University of Washington (UW). She earned her Ph.D. from Stanford University in 2009 under Professor Daphne Koller and joined UW in 2010 after serving as a Visiting Assistant Professor at Carnegie Mellon University.

She is renowned for her groundbreaking research at the intersection of AI, biology, and medicine, and widely recognized as a pioneer in explainable AI (XAI). Among her seminal contributions is the SHAP framework (Lundberg and Lee, NeurIPS’17 oral; cited over 40,000 times), which has transformed the interpretation of machine learning models across disciplines.

She has been honored with major awards, including the Samsung Ho-Am Prize in Engineering (the “Korean Nobel Prize,” as its first woman recipient in 34 years), the ISCB Innovator Award, and the NSF CAREER Award. She is an American Cancer Society Research Scholar, an AIMBE Fellow, and an ISCB Distinguished Fellow.

Her recent work advances fundamental principles of XAI and applies them to biomedicine—from uncovering molecular drivers of disease to auditing clinical AI systems—fundamentally reshaping how AI is integrated into biomedical research and healthcare. This integration has enabled novel discoveries and produced numerous awards and highly cited publications spanning AI, molecular biology, and clinical medicine.

 Professor Su-In Lee, the Boeing Endowed Professor of Computer Science at UW, earned her PhD from Stanford University in 2009 under the guidance of Professor 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 and biomedicine, Professor Lee has received prestigious accolades including the National Science Foundation (NSF) CAREER Award, the International Society for Computational Biology (ISCB) Innovator Award, and the Ho-Am Prize often referred to as the "Korean Nobel Prize," and designation as an American Cancer Society Research Scholar, a Fellow of American Institute for Medical and Biological Engineering, and ISCB Distinguished Fellow. She has also received generous grants from the National Institutes of Health, the National Science Foundation, the American Cancer Society, and Chan Zuckerberg Initiative.

Her research aims to conceptually and fundamentally advance how AI can be 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, her team uniquely focused on why a certain prediction was made by developing novel AI principles, and techniques (e.g., SHAP) to improve the interpretability of ML models, applicable to a broad spectrum of problems beyond biomedicine. 

Her recent research focuses on a broad spectrum of problems, including developing explainable AI techniques, identifying the cause and treatment of challenging diseases such as cancer and Alzheimer’s disease, and developing and auditing clinical AI models.