Gyeongchan Yun

Welcome to my page!

I'm a graduate student in System Software Laboratory at UNIST
(Advisor: Prof. Young-ri Choi)

Contact Info

E-mail: rugyoon@unist.ac.kr

Office: UNIST Engineering building 106 Room 605

Education

  • Combined M.S - Ph.D in Computer Science and Engineering, UNIST, Ulsan, Korea. (Mar. 2019 -)
  • B.S in Computer Science and Engineering (Major) and Mathematical Science (Minor), UNIST, Ulsan, Korea, Feb. 2019

Research Interests

  • Distributed System for Deep Learning
  • Machine Learning Platform
  • Big Data Analytic Platform
  • Heterogeneous Resource Management and Scheduling
  • Cloud Computing and Virtualization

Publications

  • Gyeongchan Yun, Junesoo Kang, Hyunjoon Jeong, Sanghyeon Eom, Minsung Jang and Young-ri Choi, JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs, In Proceedings of the 20th European Conference on Computer Systems (EuroSys'25), 2025. [top-tier] [paper] [code] [Acceptance rate: 12.2% (85/696)]
  • Jin Yang, Heejin Yoon, Gyeongchan Yun, Sam H. Noh, and Young‐ri Choi, A Dynamic Characteristic Aware Index Structure Optimized for Real-world Datasets, ACM Transactions on Storage, (TOS), 2025. [paper]
  • Jin Yang, Heejin Yoon, Gyeongchan Yun, Sam H. Noh, and Young‐ri Choi, DyTIS: A Dynamic Dataset Targeted Index Structure Simultaneously Efficient for Search, Insert, and Scan, Proceedings of the 18th European Conference on Computer Systems (EuroSys'23), 2023. [top-tier] [paper] [code] [Acceptance rate: 16.7% (54/323)]
  • Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, and Young‐ri Choi, HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism, Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC '20), 2020. [top-tier] [paper] [Acceptance rate: 18.7% (65/348)]

Talks & Presentations

  • JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs, the 20th European Conference on Computer Systems (EuroSys'25), 2025. [poster & presentation]
  • JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs, the 1st International Workshop on Systems and Methods for Sustainable Large-Scale AI (GreenSys), 2025. [workshop]

Work Experience

  • Treelogic (Jul. 2017 - Feb. 2018)

    • Intern Software Engineer

  • Omnious (Mar. 2017 - Jun. 2017)

    • Intern Backend Developer

  • Omnious (Jul. 2016 - Aug. 2016)

    • Web Crawler Developer

Open Source Contributions

  • PROTEUS (Jul. 2017 - Feb. 2018)

    • Developed a general data visualization library for both static and streaming data, Proteic.js [github]

    Participated in web development for Real-time Interactive Data Visualization [github]

  • TensorFlow Benchmarks

    • Fixed the error of accuracy operation in distributed all reduce mode [github]

  • DBS

    • Fixed the default run command in README.md to avoid minor error [github]

Course Projects

  • 2020 Spring (Graduate)

    • Cloud Computing: "PHD-PSGD: Probabilistic Heterogeneity-aware Decentralized Training" [paper]

  • 2019 Fall (Graduate)

    • AI System: "Hybrid Model Parallel and All-Reduce on Distributed GPU Clusters" [paper]

    Computer Vision: "Addition Residual Learning to Fully Convolutional DenseNets for Semantic Segmentation" [github]

  • 2018 Fall (Undergraduate)

    • Deep Learning: "Body Language Translator" [github]

Personal Projects