Post Doctoral Researcher

Junseon Kim is currently a Post Doctoral Researcher in the Department of Electrical and Computer Engineering at Seoul National University. He received his B.S. degree in Electronic Engineering and his M.S. degree in Electrical, Electronic, and Control Engineering from Hankyong National University (HKNU), Anseong, Gyeonggi, Republic of Korea in 2014 and 2016, respectively. He then received his Ph.D. degree in Computer Science from UNIST (Ulsan National Institute of Science and Technology), Ulsan, Republic of Korea, in 2023. His research interests include latency-guaranteed networking (with ML) for next-generation cellular networks (i.e., 5G-Adv/6G). For more information, please visit the CV site.

Jeongmin Bae is currently a Postdoctoral Researcher in the Department of Electrical and Computer Engineering at Seoul National University. She received her B.S degree in the Department of Electronic Engineering from Sogang University, Seoul, Korea, in 2017, and M.S., and Ph.D. degrees in the School of Electrical Engineering from KAIST (Korea Advanced Institute of Science and Technology), Daejeon, Korea, in 2019 and 2023, respectively.

Her research interests include real-time learning for network resource management problems, such as radio resource management and congestion control. In her research domain, she has been recognized for her learning-based scheduling work, winning the 3rd Prize in the Samsung Humantech 2019. She is actively publishing papers on scheduling with learning such as Q+ learning, which achieves throughput- and delay-optimal network resource scheduling through deep reinforcement learning. 

Seyeon Kim is currently a Post Doctoral Researcher in the Department of Electrical and Computer Engineering at Seoul National University with the Center of URAN (ITRC). He received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from KAIST (Korea Advanced Institute of Science and Technology), Daejeon, Korea, in 2014, 2016, and 2022, respectively.

His research interests include thermally reliable and energy-efficient mobile deep learning systems, neural network model compression, vertical/horizontal split computing for neural networks, and offloaded analytics for AR/XR/MR. 

He is the recipient of the best paper award of ACM MobiSys 2021 for his ground-breaking work, zTT which proposed the first application- and environment-aware DVFS with reinforcement learning for achieving zero thermal throttling in mobile systems.

Sungyong Lee is currently a Postdoctoral Researcher in the Department of Electrical and Computer Engineering at Seoul National University. He received his B.S., M.S., and Ph.D. degrees in Computer Science and Engineering from UNIST (Ulsan National Institute of Science and Technology), Ulsan, Korea, in 2014, 2016, and 2023, respectively.

His research interests include deep neural networks on mobile devices, including their training and inference, offloading neural network inference, data compression, and semantic communication. He has been recognized for his work, winning the 3rd Prize in the Samsung Humantech 2021. He has also published papers such as PERCEIVE, which predicts future uplink throughput accurately with a 2-stage LSTM (Long Short Term Memory) on mobile devices, in MobiSys 2020, and DeepVehicleSense, which exploits a staged-inference-based neural network to recognize a user's current transportation mode on mobile devices, in Transactions on Mobile Computing 2022.