Yongmun Park

Ph.D. Student at ICNL, Korea University

Profile picture of Yongmun Park

Yongmun Park is currently a Ph.D. student in the Department of Computer Science and Engineering at Korea University, working with Prof. Jeongho Kwak in the Intelligent Computing & Networking Laboratory (ICNL) at Korea University. His research focuses on resource allocation and AI-Native 6G Network Systems. In particular, he studies how to jointly manage computing resources under practical constraints such as latency, budget, and resource efficiency.

Education

  • Ph.D. Student, Department of Computer Science and Engineering, Korea University. Advisor: Jeongho Kwak. Mar. 2026 – Present.
  • M.S., inha university, Electrical and Computer Engineering. (GPA 4.13 / 4.5). Mar. 2022 – Aug. 2024.
  • B.S., inha university, Department of Electrical and Electronic Engineering. (GPA 3.6 / 4.5). Mar. 2016 – Feb. 2022.

Research Interests

  • AI-based caching in edge computing system
  • AI-Native 6G Network Systems

Publications

Journal Articles (SCIE)

  1. “Spatio-Temporal Content Caching: Leveraging Deep Learning and Stochastic Optimization,” in preparation.

Domestic Journals

  1. Yongmoon Park and Yeongjin Kim, “Flexible Content Caching Exploiting Spatio-Temporal Budget Sharing,” The Journal of Korean Institute of Communications and Information Sciences (J-KICS), vol. 49, no. 5, pp. 718–725, May 2024.
  2. Yongmoon Park and Yeongjin Kim, “Deep Learning-Based View Count Prediction for Content Caching Services,” The Journal of Korean Institute of Communications and Information Sciences (J-KICS), vol. 48, no. 12, pp. 1559–1567, Dec. 2023.

International Conferences

  1. DongYun Shin, TaeHyeong Lee, Yongmoon Park, and Yeongjin Kim, “FlexiView: Flexible Video Quality and Playout Optimization for Mobile Video Streaming,” in Proc. of IEEE SECON, Pisa, Italy, Jun. 2026, pp. 314–322. (BK21+ Outstanding International Conference).
  2. Yongmoon Park, Kyungtae Lee, Minseok Choi, and Yeongjin Kim, “Proactive Content Caching via Interplay Between Deep Learning and Stochastic Optimization,” in Proc. of IEEE MASS, Seoul, Korea, Sep. 2024, pp. 125–133. (BK21+ Outstanding International Conference).
  3. Yongmoon Park and Yeongjin Kim, “Spatio-Temporal Content Caching: Leveraging Deep Learning and Stochastic Optimization,” in Proc. of ICTC, Jeju, Korea, Oct. 2024, pp. 1946–1947.

Domestic Conferences

  1. Yongmun Park and Yeongjin Kim, “Data Mapping and Inverse Mapping for Deep Learning-based Content View Prediction,” JCCI, Busan, Korea, Apr. 2024, pp. 118–119.

Patents (Domestic)

  1. Yeongjin Kim and Yongmoon Park, “Method and System for Flexible Content Caching Exploiting Spatio-Temporal Budget Sharing,” KR 10-2909732 (Granted; Active).
  2. Yeongjin Kim and Yongmoon Park, “Method and System for Data Mapping and Inverse Mapping for Performance Enhancement of the Deep Learning for Time-Series Value Data Prediction,” KR 10-2024-0027423 (Patent Pending).

Software Registration

  • Yeongjin Kim and Yongmoon Park, “콘텐츠 캐싱 알고리즘 성능 평가 시뮬레이터,” Korea Copyright Commission, C-2024-009326, registered Mar. 11, 2024.
  • Yeongjin Kim and Yongmoon Park, “딥러닝 기반의 콘텐츠 조회수 예측 시뮬레이터,” Korea Copyright Commission, C-2024-009327, registered Mar. 11, 2024.

Projects

  • AI-Native 응용서비스 지원 AI 오케스트레이터 개발, 정보통신기술진흥센터 (IITP), Researcher, 2024.04 – 2028.12.
  • 사용자 프라이버시를 보존하는 비디오 캐싱을 위한 연합 학습 시스템, 정보통신기술진흥센터 (IITP), Researcher, 2021.12 – 2023.12.

Skills

  • Python, MATLAB

Contact

  • Email: sky123pq@korea.ac.kr
  • Office: No. 507B, Woojung Hall of Informatics, Korea University
  • Google Scholar · ORCID · ICNL