Minhee of Catholic University

The rapid expansion of the Internet of Things (IoT) is transforming sensing infrastructures into large-scale distributed cyber-physical systems. However, conventional IoT architectures remain largely cloud-centric, relying on continuous data transmission and centralized processing. Such designs introduce fundamental challenges in energy consumption, communication bandwidth, latency, and privacy exposure, particularly as the number of connected devices continues to grow. This talk introduces the emerging paradigm of the Artificial Intelligence of Things (AIoT), where intelligence is embedded directly within sensing devices through edge computing and TinyML techniques. By enabling machine learning inference on resource-constrained microcontrollers, AIoT systems can transform traditional sensors into autonomous, context-aware intelligent nodes capable of performing real-time decision making. Beyond presenting representative AIoT systems and applications, this talk also examines broader research challenges shaping the future of AIoT systems, including platform fragmentation, intelligent sensor deployment, secure federated learning, and the need for autonomous and scalable IoT architectures. In particular, we discuss how edge-intelligent AIoT systems can support emerging digital twin frameworks, enabling event-driven monitoring and scalable healthcare applications while reducing continuous data streaming. The presentation concludes with an overview of modern development pipelines for deploying machine learning models on edge devices using frameworks such as TinyML toolchains, highlighting future research opportunities in distributed intelligent sensing systems.

Biography:

Dr. Minhee Jun is an Assistant Professor in Electrical and Computer Engineering at The Catholic University of America and director of the GREENS Laboratory (General Research of End-to-end Edge-intelligent Networks and Systems). She also serves as Vice Director of the CAIR (Center for Artificial Intelligence Research). Her research lies at the intersection of Artificial Intelligence of Things (AIoT), edge intelligence, and distributed cyber-physical systems, focusing on scalable and privacy-aware intelligent sensing architectures.

She received her Ph.D. from Carnegie Mellon University, M.S. from Purdue University, and B.S. in Electrical Engineering and Mathematics from KAIST. Her recent work investigates edge-intelligent AIoT architectures and digital twin systems for healthcare monitoring, aiming to enable energy-efficient, scalable, and privacy-preserving intelligent systems.

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