معرفی
Seokhyun Chung is an Assistant Professor at the University of Virginia, located in Lab 257 of Olsson Hall. His research focuses on IoT-enabled systems, leveraging data-driven methods such as federated learning, multi-task learning, and Bayesian probabilistic modeling to address challenges in statistical heterogeneity, scalability, and personalization in connected systems. Applications span smart healthcare and manufacturing systems, emphasizing reliability and efficiency.
Research Interests: Chung's work integrates IoT, machine learning, and systems engineering to develop collaborative analytics frameworks. He explores federated learning architectures for distributed data fusion, probabilistic modeling for uncertainty-aware predictions, and optimization techniques for resource allocation in edge computing environments. His healthcare focus includes gait analysis and load estimation for wearable devices, while manufacturing applications involve job scheduling and additive manufacturing quality control.
Key Contributions: Recent work highlights include federated multi-output Gaussian processes for heterogeneous systems, real-time adaptation of time-series predictions, and fairness-aware machine learning for load carriage tasks. His interdisciplinary projects address both technical and operational challenges, such as emergency department layout optimization and toxic gas monitoring networks.
Labs & Infrastructure: Chung's research is conducted in Olsson Hall's advanced labs, leveraging IoT sensor networks, edge computing setups, and collaborative simulation environments to prototype innovative smart systems solutions.
