Dr. Eung-joo Lee: Assistant Professor, Department of Electrical and Computer Engineering; Affiliated Faculty, Opthalmology and Vision Science, University of Arizona
Wednesday, December 10, 2025,
3:00pm – 4:00pm,
ASA Great Room and Zoom
Recent advances in deep learning have transformed computer vision, driving progress in autonomous navigation, surgical systems, and medical image analysis. However, achieving real-time intelligence in resource-limited settings such as wearable devices and embedded platforms remains challenging. In his presentation Dr. Lee will explain how new research makes AI smarter and more practical by solving two problems. First, it helps AI learn from less data by using unlabeled information and computer-generated examples, instead of needing thousands of hand-labeled training samples. Second, it creates smaller, faster AI models that can run on phones, medical devices, and robots rather than requiring powerful computers. The goal is building AI systems for healthcare and self-driving technology that understand the world like humans do but work on everyday devices with limited training data.
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