Youngmok Jung

Youngmok Jung

PhD student

About Me

My research focuses on adapting artificial intelligence approaches into traditional systems.

Research Interest

Deep Learning, Next Generation Sequencing, Video Delivery, and Distributed System

Publications

[2021 EuroSys] Towards Timeout-less Transport in Commodity Datacenter Networks

Acceptance rate 27/191: 14%

Hwijoon Lim, Wei Bai, Yibo Zhu, Youngmok Jung and Dongsu Han

Link to Website

[2020 MOBICOM] NEMO: Enabling Neural-enhanced Video Streaming on Commodity Mobile Devices

Acceptance rate 62/384: 16%

Hyunho Yeo, Chan Ju Chong, Youngmok Jung, Juncheol Ye and Dongsu Han

Link to Website

[2020 SIGCOMM] LiveNAS - Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning

Acceptance rate 53/250: 21%

Jaehong Kim, (CO-First)Youngmok Jung, Hyunho Yeo, Juncheol Ye and Dongsu Han

Link to Website

[2018 OSDI] NAS - Neural Adaptive Streaming

Acceptance rate 47/257: 18%

Hyunho Yeo, Youngmok Jung, Jaehong Kim, Jinwoo Shin, and Dongsu Han

Link to Website

Research Projects

Short Read Alignment software for Next-generation Sequencing

- Developed a short read alignment tool that is the first to leverage learned indices to find exact match of short substring (0-300 length) in the long reference DNA (6 billion length for human genome).
- Achieved up to 3.45x speedup in throughput over state-of-the-art algorithm by reducing the number of instructions by 4.60x, memory accesses by 8.77x, and LLC misses by 2.21x, while ensuring identical output.

Link to Github

Deep Learning-based Live Video Streaming (SIGCOMM 2020)

- Implemented new APIs in C++ WebRTC open-source code from Google; 1) to obtain decoded and encoded frames with the frame timestamp. 2) to control and obtain bandwidth information in the WebRTC.
- Used Python wrapper (CPython) to integrate the newly added API to the LiveNAS system.
- Developed the LiveNAS system which involves; 1) Online training and inferencing super-resolution DNN model during the live video stream. 2) Transmitting training data over RTP. 3) Controlling bandwidth allocation

Link to Website

Timeout-less Transport Protocol (EuroSys 2021)

- Implemented NS-3 simulation code[C++] for RDMA protocol and TLT.
- Managed switch configuration which involves priority-flow-control, Shared Buffer Pool size, and Packet Color.

Link to Github

Deep Learning-based Internet Video Delivery (OSDI 2018)

- Developed an integrated-ABR (adaptive bit-rate) controller code[Python,Tensorflow] based on Reinforcement Learning (actor-critic framework of A3C).
- Developed simulation code for video delivery to train RL model and measure user QoE (Quality of Experience).
- Implemented evaluation code to run 5,832 hours of real-time video streaming in Google Cloud in just a few days.

Link to Website