Contributing my knowledge and experience of 16-years of industry experience to the academic sector has been successfully demonstrated through publications in top-tier venues, such as IEEE Internet of Things Journal, IEEE Transactions on Cloud Computing, and IEEE Access; furthermore, my current research regarding fast model convergence and low-carbon in Machine learning has shown the progress, such as the publication in ICOIN23.
Moreover, with over sixteen years of industry experience, I have been a full-cycle researcher from abstract ideas to the system development of cross-functional experiences, such as the ML- and Networking-based knowledge (i.e., 17 patents, standardization activities in IETF and Zigbee Alliance) and technology-driven business experiences (i.e., funding proposals for ITER, product management, project supervising, and business development).
My research interests are resource-efficient methodologies for training edge intelligence, joint utility optimization among the stakeholders participating in machine learning in the wireless and wired network, and resource allocation methodologies of offloading decisions for low latency and scalability.
I received a B.S. degree from Sungkyunkwan University (SKKU), Korea, in February 1998 and an M.S. degree from the University of Southern California (USC), Los Angles, USA, in 2002. I earned my Ph.D. from SKKU in August 2019 and am a senior researcher working in Autonomous Distributed Systems Lab at Umea University.