Youngmok Jung

tom418@kaist.ac.kr | Google Scholar | CV

Youngmok Jung

I am a computer science PhD candidate at INA lab, KAIST advised by Dongsu Han. Before then, I received my BS degree in Electrical Engineering in KAIST, South Korea.

I work on developing real-world systems that use AI/ML approaches. Real-world systems have assumptions that are often different from those of AI/ML approaches. My works aims at 1) designing new system that uses AI/ML approaches; 2) improving the AI/ML approaches leveraging the assumptions in the system. All my work involves several months of implementation followed by thorough testing in real-world data.

Recently, I am striving to redesign genomics software using AI/ML approaches. In the past, I worked on various topics in image super-resolution based on neural network and video delivery system.

Awards and Honors

Feb, 2022 SAMSUNG PhD Scholarship :smile:
Spring, 2021 KAIST Breakthrough of the Year 2021, Spring (LiveNAS, NEMO)

Selected Publications

  1. Bioinformatics
    BWA-MEME: BWA-MEM emulated with a machine learning approach
    Youngmok Jung and Dongsu Han
    Oxford Bioinformatics, 2022
    SIGCOMM
    LiveNAS - Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning
    Jaehong Kim, (Co-first)Youngmok Jung, Hyunho Yeo, Juncheol Ye and Dongsu Han
    In Proceedings of ACM SIGCOMM conference, 2020

    Publications

    1. CoNEXT
      Co-optimizing for Flow Completion Time in Radio Access Network
      Jaehong Kim, Yunheon Lee, Hwijoon Lim, Youngmok Jung, Song Min Kim, and Dongsu Han
      In Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies, 2022
      SIGCOMM
      NeuroScaler: Neural Video Enhancement at Scale
      Hyunho Yeo, Hwijoon Lim, Jaehong Kim, Youngmok Jung, Juncheol Ye, and Dongsu Han
      In Proceedings of ACM SIGCOMM conference, 2022
      EuroSys
      Towards Timeout-less Transport in Commodity Datacenter Networks
      Hwijoon Lim, Wei Bai, Yibo Zhu, Youngmok Jung and Dongsu Han
      The 16th European Conference on Computer Systems, 2021
      Mobicom
      NEMO: Enabling Neural-enhanced Video Streaming on Commodity Mobile Devices
      Hyunho Yeo, Chan Ju Chong, Youngmok Jung, Juncheol Ye and Dongsu Han
      The 26th Annual International Conference on Mobile Computing and Networking, 2020
      OSDI
      Neural Adaptive Content-aware Internet Video Delivery
      Hyunho Yeo, Youngmok Jung, Jaehong Kim, Jinwoo Shin, and Dongsu Han
      13th USENIX Symposium on Operating Systems Design and Implementation, 2018

    Experiences

    1. Adapting AI & ML to System

      - Used image Super-resolution deep learning model to enhance the video streaming system. Various techniques (e.g. Online training, Data specialization) were used to build practical system (LiveNAS, NAS)
      - Developed integrated ABR algorithm using Reinforcement-learning (a3c) that predicts the network bandwidth and optimize the user QoE in video streaming. (NAS)
      - Developed new learned-index model that adapts to imbalanced DNA dataset. (BWA-MEME)

      System Design and Development

      - Developed new live video streaming system (LiveNAS) that delivers live video with the same quality as WebRTC using only 45.9% bandwidth on average. LiveNAS system is built on top of Google WebRTC.
      - Developed new short-read alignment software (BWA-MEME) using learned-index that achieved up to 3.45x speedup over state-of-the-art algorithm.
      - Developed VoD (video-on-demand) streaming simulation environment for RL model. Also, validated the RL model works well on the real world after training on the simulation environment.
      - Implemented network protocols (TLT, PFC) in NS-3 network simulator.

      Opensource

        BWA-MEME

        https://github.com/kaist-ina/BWA-MEME
        - Short-read alignment software, released for benefit of research community.

        Skills

          Programming Languages: Python, C, C++, SQL, UNIX shell scripting
          Frameworks: Tensorflow, Pytorch, Spark, Hadoop, Python Django, Cloudstack