The DSI Distinguished Speaker Series will highlight expert researchers who are applying data, machine learning, and computational systems to a broader scientific discipline.

Guest Speaker

Su-In Lee, Associate Professor, Paul G. Allen School of Computer Science & Engineering, University of Washington

Details

March 29, 2021 (2:00 PM – 3:00 PM ET) – Online Event
REGISTER HERE

Hosted By

DSI Postdoctoral Researchers

About the Seminar

Explainable Artificial Intelligence for Biology and Health

Abstract: Modern machine learning (ML) models can accurately predict patient progress, an individual’s phenotype, or molecular events such as transcription factor binding.  However, they do not explain why selected features make sense or why a particular prediction was made. For example, a model may predict that a patient will get chronic kidney disease, which can lead to kidney failure.  The lack of explanations about which features drove the prediction – e.g., high systolic blood pressure, high BMI, or others – hinders medical professionals in making diagnoses and decisions on appropriate clinical actions.  I will briefly describe my group’s efforts to develop interpretable ML techniques for varied biological and medical applications, including treating cancer based on a patient’s own molecular profile, identifying therapeutic targets for Alzheimer’s, predicting kidney diseases, preventing complications during surgery, enabling pre-hospital diagnoses for trauma patients, and improving our understanding of pan-cancer biology and genome biology.

Bio: Prof. Su-In Lee is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering. 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.

The research done by Prof. Lee’s group has conceptually and fundamentally advanced how AI can be integrated with biomedicine by addressing novel, forward-looking, and stimulating questions, enabled by AI possibilities. For example, although the primary focus of AI applications in the field of medicine had been on accurately predicting a patient’s phenotype (e.g., predicting the response to certain chemotherapy based on the patient’s gene expression profile), Prof. Lee focused on why a certain prediction was made, which can point to the molecular mechanisms underlying patient’s phenotype (e.g., drug sensitivity). 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 molecular biomedicine, to address new questions and make novel discoveries from high-throughput molecular data or patient’s medical record data.