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

Lee is internationally recognised for her contributions to artificial intelligence, biology, and medicine. She is widely regarded as a pioneer of explainable artificial intelligence (XAI), a field that seeks to make complex machine learning systems transparent, interpretable, and trustworthy. Her seminal work on the SHAP framework established foundational principles for interpreting machine learning models and is now one of the most widely used approaches to explainable AI in academia, industry, and healthcare.

Her research advances both the foundations of artificial intelligence and its applications to biology and medicine. She has developed fundamental methods for interpretable AI while also pioneering their use to uncover biological mechanisms, accelerate scientific discovery, and improve healthcare. By combining methodological innovation with real-world impact, her work has generated new insights into aging, cancer, Alzheimer’s disease, and a wide range of other diseases and biological processes, while deepening our understanding of how increasingly complex AI systems can be interpreted, audited, and directed.

Her contributions have been recognised through numerous honors, including the Samsung Ho-Am Prize in Engineering — South Korea’s highest honor in engineering — which she received as the first woman in the category in 34 years. She has also received the ISCB Innovator Award and the NSF CAREER Award, and is an American Cancer Society Research Scholar, a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), and a Distinguished Fellow of the International Society for Computational Biology (ISCB).

Through her interdisciplinary scholarship, Lee has helped shape the field of explainable AI and shown how advances in artificial intelligence can drive scientific discovery and improve human health.