Join us on Thursday, February 27, 2026, at 12:00 PM PT/3:00 PM ET for the lecture: “From Volume to Value: Rethinking Data for AI in Healthcare” by Dr. Teresa Wu. This lecture is part of the Bridge2AI Training, Recruitment, and Mentoring (TRM) 2025-26 Lecture Series Novel AI Technology Module.

Dr. Teresa Wu is the Fulton Professor of Industrial Engineering, the Vice Dean for Academic and Student Affairs at Ira Fulton Schools of Engineering (FSE), Arizona State University. She is also the founding Director of the ASU–Mayo Center for Innovative Imaging (AMCII), a multi-institutional center uniting ASU engineers and data scientists with clinicians at Mayo Clinic, Arizona. Her research focuses on machine learning and deep learning for heterogeneous, multi-modal medical data, with applications in disease diagnosis and prognosis.

Dr. Wu is a President’s Professor at ASU (2024) and an IISE Fellow (2020), and has received honors including the IBM Faculty Research Award in Health Systems (2017), the Harold G. Wolff Lecture Award at Mayo Clinic (2015), and the Fulton Schools Exemplar Award at ASU (2016). She was also an ASU PLuS Global Health Alliance Fellow (2016–2020) and an NSF CAREER Award recipient (2003). She serves as the Emeritus Editor-in-Chief of IISE Transactions on Healthcare Systems Engineering; Associate Editor forJournal of Alzheimer’s Disease,Neuroscience and Biomedical Engineering, and IIE Transactions on Healthcare Engineering. She is an active contributor to the research community through editorial leadership, program committees for NIPS, SDM, and KDD, long-standing NSF grant review service, and a member of the Institute of Industrial and Systems Engineers.

Lecture Learning Objectives:

• Review the evolution of AI methodologies, with a focus on modeling approaches in quantitative medical imaging.
• Review the impact of data quality versus data quantity on the performance and reliability of AI models in medical applications.
• Learn how post-hoc calibration improves prediction with confidence.

Introduction