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Assistant Professor of Electrical and Computer Engineering, University of Arizona
Deep Learning-based (Medical) Image Analysis on Resource-Constrained Systems
Abstract: Recent advances in deep learning have significantly improved the performance of computer vision systems across domains ranging from autonomous navigation to medical image analysis. However, realizing these capabilities in real-time, resource-constrained environments, including surgical systems, wearable health devices, and embedded platforms, remains a challenge. In this seminar, I will discuss how AI integrates perception, decision, and control through efficient and robust visual intelligence. The presentation will focus on two directions: (1) data-centric learning for model robustness, including the use of self-supervised and synthetic data generation techniques to overcome the limitations of labeled datasets, and (2) computationally efficient model development for practical deployment, highlighting lightweight network architectures and knowledge distillation for embedded and edge devices. By integrating these directions, I aim to advance multimodal perception and decision-making systems that bridge algorithmic innovation and translational impact, moving toward scalable and human-centric intelligence in both autonomous and medical environments.
Bio: Dr. Eungjoo Lee is an assistant professor in the Department of Electrical and Computer Engineering at the University of Arizona and is affiliated with the Department of Ophthalmology and Vision Science. Before joining at the University of Arizona, Dr. Lee was a postdoctoral research fellow at MGH/Harvard Medical School, where he was a member of the Center for Machine Learning at Martinos. He completed his Ph.D. in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. During his doctoral studies, he engaged in a research internship at the U.S. DEVCOM Army Research Laboratory and collaborated on interdisciplinary research with Children’s National Hospital, Washington, D.C.