Speaker: Wei-Lun (Harry) Chao, associate professor at the Ohio State University
Date and Time: October 31, 2:00-3:00 p.m.
Location: Coda 114
Host: Kai Wang

Title: Interpretable AI: A Goal-Oriented, User-Centric Perspective

Abstract: How do deep learning “vision” models arrive at their predictions? This fundamental question has motivated a vibrant line of work in explainable and interpretable AI (XAI). In this talk, I will introduce XAI from a goal-oriented, user-centric perspective, emphasizing explanations that serve scientists’ and practitioners’ needs rather than merely optimizing proxy metrics. I will then showcase milestones from Imageomics (https://imageomics.osu.edu/)—an emerging interdisciplinary field to make biological traits computable from images—including Finer-CAM and interpretable transformers for precise, fine-grained attribution; sparse autoencoders (SAE) that uncover meaningful features for scientific discovery; BioCLIP, a vision foundation model built for the Tree of Life; and Static Segmentation by Tracking (SST) for specimen image analysis. I will conclude with a discussion on the future research opportunities in this exciting frontier.

Bio: Wei-Lun (Harry) Chao (https://cse.osu.edu/people/chao.209) is an Associate Professor and College of Engineering Innovation Scholar in Computer Science and Engineering at The Ohio State University (OSU). His research focuses on machine learning and computer vision, with applications spanning visual recognition, autonomous driving, biology, and healthcare. He aims to develop fundamental understandings and robust, widely applicable algorithms to tackle real-world challenges. He is particularly interested in learning from imperfect data, including limited, noisy, heterogeneous, distribution-shifting, and inaccessible data. His contributions have been recognized by several awards and honors, including the OSU Early Career Distinguished Scholar Award (2025) and CVPR Best Student Paper Award (2024). Before joining OSU in 2019, he was a Postdoctoral Associate at Cornell University (2018–2019), working with Kilian Weinberger and Mark Campbell. He earned his Ph.D. in Computer Science from the University of Southern California (2013–2018) under the supervision of Fei Sha.