Seyeon Kim, Ph.D.
Seyeon Kim, Ph.D.
Assistant Professor (Dept. of Computer Science and Engineering, Korea University, South Korea)
📧 seyeon625@korea.com
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Seyeon Kim is currently an Assistant Professor in the Computer Science and Engineering department at Korea University. Previously, he was a Postdoctoral Researcher in the Electrical and Computer Engineering department at Seoul National University (SNU) and later in the Computer Science department at the University of Colorado Boulder (USA). 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 2015, 2017, 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 groundbreaking work, zTT, which proposed the first application- and environment-aware DVFS with reinforcement learning to achieve zero thermal throttling in mobile systems.
Current research interest: Near-memory networking/computing, Thermally-reliable wireless networking, Real-time (volumetric) video streaming, AI-based cloud management
Education
Education
B.S., EE, KAIST, South Korea, 2015
M.S., EE, KAIST, South Korea, 2017 (Advisor: Prof. Song Chong (KAIST))
Ph.D., EE, KAIST, South Korea, 2022 (Advisor: Prof. Song Chong (KAIST), Co-advised by Prof. Kyunghan Lee (SNU))
Career
Career
Postdoctoral Researcher, Networked Computing and AI Lab (Host: Prof. Kyunghan Lee), ECE, Seoul National University, South Korea, 2022.09 - 2023.12
Postdoctoral researcher, Internet Systems Lab (Host: Prof. Sangtae Ha), CS, University of Colorado at Boulder, USA, 2024.02 - 2025.02
Academic Activity
IEEE MASS'24 (TPC Member), IEEE CCNC'25 (TPC Member)
Awards
Awards
ACM Mobisys Best Paper Award 2021
Awarded for the paper: "zTT: Learning-based DVFS with Zero Thermal Throttling for Mobile Devices"
Talks
Talks
On Designing a Thermally-reliable Mobile System (A3 Foresight Workshop for AI-based Future IoT Technologies and Services, Tokyo, Japan, 2022.12.19)
On Learning-based Mobile Performance Guarantee under Time-varying Resource Constraints (DGIST, 2022.08.26)
강화 학습의 기초 및 응용 (머신러닝/강화학습의 기초 및 응용 강좌, KICS, Online, 2022.07.04)
zTT: Learning-based DVFS with Zero Thermal Throttling for Mobile Devices (A3 Foresight Workshop, Online, 2021.08.11)
심층 강화 학습과 응용 사례 (제1회 강화학습 기초 및 응용 강좌, KICS, Online, 2020.08.24)
Publications
Publications
(Corresponding author *)
Kyungmin Bin, Seyeon Kim, Sangtae Ha, Song Chong, Kyunghan Lee. "NeuroBalancer: Balancing System Frequencies with Punctual Laziness for Timely and Energy-efficient DNN Inferences" (IEEE TMC 2025)
Seonwoo Kim, Yoonsung Nam, Minwoo Park, Heewon Lee, Seyeon Kim*, Sangtae Ha. “Dejavu: Reinforcement Learning-based Cloud Scheduling with Demonstration and Competition” 2024 IEEE 21th International Conference on Mobile Ad Hoc and Smart Systems (MASS). IEEE, 2024. (IEEE MASS 2024)
Taeho Kim, Yanming Wang, Vatshank Chaturvedi, Lokesh Gupta, Seyeon Kim*, Yongin Kwon, Sangtae Ha. "LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs." IJCAI. 2024. (Long talk (Top 2%))
Kyungmin Bin, Jongseok Park, Chanjeong Park, Seyeon Kim, Kyunghan Lee. "CoActo: CoActive Neural Network Inference Offloading with Fine-grained and Concurrent Execution." Proceedings of the 22th Annual International Conference on Mobile Systems, Applications, and Services. 2024. (ACM Mobisys 2024)
Seyeon Kim, Kyungmin Bin, Donggyu Yang, Sangtae Ha, Kyunghan Lee, Song Chong. “ENTRO: Tackling the Encoding and Networking Trade-off in Offloaded Video Analytics” Proceedings of the 31th ACM International Conference on Multimedia. 2023. (ACM Multimedia 2023)
Seyeon Kim, Kyungmin Bin, Sangtae Ha, Kyunghan Lee, Song Chong. “zTT: learning-based DVFS with zero thermal throttling for mobile devices.” GetMobile: Mobile Computing and Communications 25.4 (2022): 30-34. (ACM GetMobile 2022, Invited paper (Highlight))
Insoo Lee, Seyeon Kim, Sandesh Dhawaskar Sathyanarayana, Kyungmin Bin, Song Chong, Kyunghan Lee, Drik Grunwald, Sangtae Ha. “RL-based FEC Adjustment for better QoE in WebRTC.” Proceedings of the 30th ACM International Conference on Multimedia. 2022. (ACM Multimedia 2022, Oral (Top 2%))
Seyeon Kim, Kyungmin Bin, Sangtae Ha, Kyunghan Lee, Song Chong. “zTT: learning-based DVFS with zero thermal throttling for mobile devices.” Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services. 2021. (ACM Mobisys 2021, Best Paper)
Workshop & Demo
Workshop & Demo
Max Hollingsworth, Yaguang Zhang, Todd Schumann, Chris Anderson, Michael Cotton, Seyeon Kim, Sangtae Ha, Dirk Grunwald. “Repurposing Cellular Reference Signals: Accurate RSRP Measurements with Mobile Phones.” IEEE International Symposium on Dynamic Spectrum Access Networks (IEEE DySPAN Workshop)
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