[ https://lsi.princeton.edu/events/2025/qcb-seminar-su-lee-university-washingt... | QCB Seminar with Su-In Lee, University of Washington ] Monday, October 6, 2025 3:00 – 4:00 p.m. Carl Icahn Lab, 101 “ Explainable AI for health: where we are and how to move forward. ” The first part of my talk explores various research initiatives undertaken by my lab, emphasizing the principles and techniques of explainable AI and their application across a range of biomedical fields. I will demonstrate how explainable AI can elucidate novel scientific inquiries, with a primary emphasis on understanding neurodegenerative diseases and biological age. In the second part, we will explore the evolving landscape of explainable AI, uncovering its potential to chart new scientific directions in biomedicine, exemplified by our recent work in dermatology, emergency medicine and precision cancer medicine. This discussion aims to shed light on the necessary enhancements for explainable AI to effectively tackle a wide array of real-world challenges in biomedicine. BQ_BEGIN 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 [ https://aims.cs.washington.edu/su-in-lee | numerous awards ] and [ https://aims.cs.washington.edu/publications | highly cited publications ] spanning AI, molecular biology, and clinical medicine. Hosted by Yuri Pritykin and Mona Singh BQ_END BQ_BEGIN BQ_END