Myeongjae Jeon

Research Team: OMNIA

Email: mj.jeon_at_postech.ac.kr

I am an associate professor in the Computer Science and Engineering Department and Graduate School of Artificial Intelligence at POSTECH. Previously, I was an associate professor at UNIST and a visiting professor in the DeepSpeed team at Microsoft. Prior to joining UNIST in 2018 fall, I spent several years in industry with Systems Research Group at Microsoft Research (2015 - 2018) and with Systems Research Group at the ARM Research (2014 - 2015). My prior research work has been deployed in several production systems in Microsoft, including Bing search engine, Open Platform for AI (OpenPAI), and Azure telemetry monitoring system, bringing real-world impacts from systems research. Currently at POSTECH, I focus on AI systems, real-time big data analytics, and systems for new HW, with my team members at OMNIA Lab.

I finished my Ph.D. in Computer Science at Rice University in May 2014, under the supervision of Prof. Alan L. Cox and Prof. Scott Rixner. During my Ph.D. I actively collaborated with Dr. Yuxiong He and Dr. Sameh Elnikety through four internships at Microsoft Research. I received the M.S. degree in computer science from KAIST and the B.E. degree in computer engineering from Kwangwoon University.

I won the Best Paper Awards from ICDE 2022 and SYSTOR 2016 and was selected twice as a Meta Faculty Research Award Finalist in 2020 and 2022.

I am looking for self-motivated and hard-working students and currently have openings for undergraduate internships and graduate-level studies. If you're interested in building system platforms for AI & big data processing, cloud computing, and various emerging hardware, please reach out by introducing yourself (with your CV and transcript). I highly encourage prospective graduate students to consider an internship with us "before" applying to POSTECH.

News

Feb. 2025 Serving as the PC at MICRO'25

Jan. 2025 Serving as the PC at ASPLOS'26

Nov. 2024 Serving as the General Chair at APSys'25

Sep. 2024 Serving as the PC at ICDCS'25 (Distributed Systems for AI/ML track)

Aug. 2024 Joined POSTECH as an Associate Professor

May 2024 Serving as the PC at HPCA'25

Feb. 2024 Serving as the PC at APSys'24

Dec. 2023 Serving as the PC at ICDCS'24 (AI/ML for Distributed Systems track)

Sep. 2023 Serving as the PC at SYSTOR'24

May 2023 Giving a talk on systems for on-device continual learning at POSTECH and Ajou U seminars

Oct. 2022 My research team and myself are featured in UNIST magazine (link)

Oct. 2022 Serving as the PC at ICDCS'23 (AI for Systems and Systems for AI track)

Sep. 2022 Selected as a Facebook Faculty Research Award finalist

May 2022 Received Best Paper Award from ICDE'22 (news)

Apr. 2022 Giving a talk on systems for continual learning and ML data augmentation at LG AI Research Lab

Feb. 2022 Giving a talk on streaming analytics with adaptive near-data processing at Computer System Society

Oct. 2021 Serving as the PC at ICDCS'22 (ML on or for Distributed Systems track)

Oct. 2021 Serving as a vice session chair at SOSP'21 (Learning track)

Sep. 2021 Selected as one of 7 AI research labs by Kakao Brain (We are the only lab in AI systems: news)

Nov. 2020 Serving as the PC at ICDCS'21 (ML on or for Distributed Systems track)

Jul. 2020 Selected as a Facebook Faculty Research Award finalist

Apr. 2020 Giving a talk on our ML systems research at Samsung Electronics (for Data & IT center)

Jan. 2020 Serving as the PC at ICDCS'20 (Distributed & Federated Learning track)

Oct. 2019 Attending Rice CS 35th Anniversary as a panel speaker for "The Role of Programming Systems while Hardware Specialization is Exploding" with Kathryn, Mary, Dan, and Felix

Teaching

Computer architecture 2023 fall, 2021 fall

Operating systems 2021 spring, 2020 spring

Parallel computing 2022 fall, 2019 spring

Data structure 2020 fall, 2019 fall, 2018 fall

Big data systems (graduate) 2021 fall

AI systems (graduate) 2024 fall, 2022 fall, 2020 fall, 2019 spring

Publications

Training-Free Exponential Context Extension via Cascading KV Cache
1. - Jeffrey Willette, Heejun Lee, Youngwan Lee, Myeongjae Jeon, Sung Ju Hwang
  - ICLR, Apr. 2025 [PDF]
A Training-Free Sub-quadratic Cost Transformer Model Serving Framework with Hierarchically Pruned Attention
1. - Heejun Lee, Geon Park, Youngwan Lee, Jaduk Suh, Jina Kim, Wonyong Jeong, Bumsik Kim, Hyemin Lee, Myeongjae Jeon, Sung Ju Hwang
  - ICLR, Apr. 2025 [PDF]
REP: Resource-Efficient Prompting for On-device Continual Learning
1. - Sungho Jeon, Xinyue Ma, Kwang In Kim, Myeongjae Jeon
  - Preprint, Jun. 2024 [PDF]
FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation
1. - Taeyoon Kim, Chanho Park, Mansur Mukimbekov, Heelim Hong, Minseok Kim, Ze Jin, Changdae Kim, Ji-Yong Shin, Myeongjae Jeon
  - VLDB, Aug. 2024 [PDF]
Metis: Fast Automatic Distributed Training on Heterogeneous GPUs
1. - Taegeon Um, Byungsoo Oh, Minyoung Kang, Woo-Yeon Lee, Goeun Kim, Dongseob Kim, Youngtaek Kim, Mohd Muzzammil, Myeongjae Jeon
  - USENIX ATC, Jul. 2024 [PDF]
Blaze: Holistic Caching for Iterative Data Processing
1. - Won Wook Song, Jeongyoon Eo, Taegeon Um, Myeongjae Jeon, Byung-Gon Chun
  - EuroSys, Apr. 2024 [PDF]
Cost-effective On-device Continual Learning over Memory Hierarchy with Miro
1. - Xinyue Ma, Suyeon Jeong, Minjia Zhang, Di Wang, Jonghyun Choi, Myeongjae Jeon
  - ACM MobiCom, Oct. 2023 [PDF][Slides][Code]
Sponge: Fast Reactive Scaling for Stream Processing with Serverless Frameworks
1. - Won Wook Song, Taegeon Um, Sameh Elnikety, Myeongjae Jeon, Byung-Gon Chun
  - USENIX ATC, Jul. 2023 [PDF][Slides][Code]
EnvPipe: Performance-preserving DNN Training Framework for Saving Energy
1. - Sangjin Choi, Inhoe Koo, Jeongseob Ahn, Myeongjae Jeon, Youngjin Kwon
  - USENIX ATC, Jul. 2023 [PDF][Slides][Code]
SWAN: WAN-aware Stream Processing on Geographically-distributed Clusters
1. - Won Wook Song, Myeongjae Jeon, Byung-Gon Chun
  - ACM APSys, Aug. 2022 [PDF]
Sibylla: To Retry or Not To Retry on Deep Learning Job Failure
1. - Taeyoon Kim, Suyeon Jeong, Jongseop Lee, Soobee Lee, Myeongjae Jeon
  - USENIX ATC, Jul. 2022 [PDF][Slides][Talk]
Memory Harvesting in Multi-GPU Systems with Hierarchical Unified Virtual Memory
1. - Sangjin Choi, Taeksoo Kim, Jinwoo Jeong, Rachata Ausavarungniurn, Myeongjae Jeon, Youngjin Kwon, Jeongsub Ahn
  - USENIX ATC, Jul. 2022 [PDF][Slides][Talk][Code]
CarM: Hierarchical Episodic Memory for Continual Learning
1. - Soobee Lee, Minindu Weerakoon, Jonghyun Choi, Minjia Zhang, Di Wang, Myeongjae Jeon
  - DAC, Jul. 2022 [PDF][PDF(extended)][Code]
Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing
1. - Atul Sandur, ChanHo Park, Stavros Volos, Gul Agha, Myeongjae Jeon
  - IEEE ICDE, May 2022 (Best Paper) [PDF][PDF(extended)][Code]
Streaming Analytics with Adaptive Near-data Processing
1. - Atul Sandur, ChanHo Park, Stavros Volos, Gul Agha, Myeongjae Jeon
  - EMDC, Apr. 2022 [PDF] (Invited paper)
Zico: Efficient GPU Memory Sharing for Concurrent DNN Training
1. - Gangmuk Lim, Jeongseob Ahn, Wencong Xiao, Youngjin Kwon, Myeongjae Jeon
  - USENIX ATC, Jul. 2021 [PDF][Talk][Slides][Code/Benchmark]
Reliability of Large-scale GPU Clusters for Deep Learning Workloads
1. - Junjie Qian, Taeyoon Kim, Myeongjae Jeon
  - EMDC, Apr. 2021 [PDF] (Invited paper)
Approximate Quantiles for Datacenter Telemetry Monitoring
1. - Gangmuk Lim, Mohamed Hassan, Ze Jin, Stavros Volos, Myeongjae Jeon
  - IEEE ICDE, Apr. 2020 [PDF] [PDF(extended)][Talk][Slides] (Short paper)
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
1. - Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang
  - USENIX ATC, Jul. 2019 [PDF][Slides][Talk][Trace]
StreamBox-HBM: Stream Analytics on High Bandwidth Hybrid Memory
1. - Hongyu Miao, Myeongjae Jeon, Gennady Pekhimenko, Kathryn S. McKinley, Felix Xiaozhu Lin
  - ASPLOS, Apr. 2019 [PDF][Talk][Slides][Code]
Tiresias: A GPU Cluster Manager for Distributed Deep Learning
1. - Juncheng Gu, Mosharaf Chowdhury, Kang G. Shin, Yibo Zhu, Myeongjae Jeon, Junjie Qian, Hongqiang Liu, Chuanxiong Guo
  - NSDI, Feb. 2019 [PDF][Talk][Slides][Simulator]
Accelerated Training for CNN Distributed Deep Learning through Automatic Resource-Aware Layer Placement
1. - Jay H. Park, Sunghwan Kim, Jinwon Lee, Myeongjae Jeon, Sam H. Noh
  - Preprint, Jan. 2019 [PDF]
TerseCades: Efficient Data Compression in Stream Processing
1. - Gennady Pekhimenko, Chuanxiong Guo, Myeongjae Jeon, Peng Huang, Lidong Zhou
  - USENIX ATC, Jul. 2018 [PDF][Slides]
StreamBox: Modern Stream Processing on a Multicore Machine
1. - Hongyu Miao, Heejin Park, Myeongjae Jeon, Gennady Pekhimenko, Kathryn S. McKinley, Felix Xiaozhu Lin
  - USENIX ATC, Jul. 2017 [PDF][Slides][Code]
SSD Failures in Datacenters: What, When and Why?
1. - Iyswarya Narayanan, Di Wang, Myeongjae Jeon, Bikash Sharma, Laura Caulfield, Anand Sivasubramaniam, Ben Cutler, Jie Liu, Badriddine Khessib, Kushagra Vaid
  - ACM SIGMETRICS / IFIP Performance, Jun. 2016 [PDF] (Poster)
  - ACM SYSTOR, Jun. 2016 (Best Student Paper) [PDF]
TPC: Target-Driven Parallelism Combining Prediction and Correction to Reduce Tail Latency in Interactive Services
1. - Myeongjae Jeon, Yuxiong He, Hwanju Kim, Sameh Elnikety, Scott Rixner, Alan L. Cox
  - ASPLOS, Apr. 2016 [PDF][Slides]
Predictive Parallelization: Taming Tail Latencies in Web Search
1. - Myeongjae Jeon, Saehoon Kim, Seung-Won Hwang, Yuxiong He, Sameh Elnikety, Alan L. Cox, Scott Rixner
  - ACM SIGIR, Jul. 2014 [PDF][Slides(pptx)]
Reducing DRAM Row Activations with Eager Read/Write Clustering
1. - Myeongjae Jeon, Conglong Li, Alan L. Cox, Scott Rixner
  - ACM Transactions on Architecture and Code Optimization (TACO), Dec. 2013 [PDF]
Adaptive Parallelism for Web Search
1. - Myeongjae Jeon, Yuxiong He, Sameh Elnikety, Alan L. Cox, Scott Rixner
  - EuroSys, Apr. 2013 [PDF][Slides(pptx)]
Workload Characterization and Performance Implications of Large-Scale Blog Servers
1. - Myeongjae Jeon, Youngjae Kim, Jaeho Hwang, Joonwon Lee, Euiseong Seo
  - ACM Transactions on the Web (TWEB), Nov. 2012 [PDF]
Energy Reduction in Consolidated Servers Through Memory-Aware Virtual Machine Scheduling
1. - Jae-Wan Jang, Myeongjae Jeon, Hyo-Sil Kim, Heeseung Jo, Jin-Soo Kim, Seungryoul Maeng
  - IEEE Transactions on Computers (TC), Apr. 2011 [PDF]
Replicated Abstract Data Types: Building Blocks for Collaborative Applications
1. - Hyun-Gul Roh, Myeongjae Jeon, Jinsoo Kim, Joonwon Lee
  - Journal of Parallel and Distributed Computing (JPDC), Mar. 2011 [PDF]
Measurement, Modeling, and Analysis of a Large-scale Blog Server Workload
1. - Myeongjae Jeon, Jaeho Hwang, Youngjae Kim, Jae-wan Jang, Joonwon Lee, Euiseong Seo
  - IEEE SocialCom SCA, Aug. 2010 [PDF]
Log' version vector: Logging version vectors concisely in dynamic replication
1. - Hyun-Gul Roh, Myeongjae Jeon, Euiseong Seo, Jinsoo Kim, Joonwon Lee
  - Elsevier Information Processing Letters (IPL), May. 2010 [PDF]
Guest-Aware Priority-based Virtual Machine Scheduling for Highly Consolidated Server
1. - Dongsung Kim, Hwanju Kim, Myeongjae Jeon, Euiseong Seo, Joonwon Lee
  - Euro-Par, Aug. 2008 [PDF]
Domain Level Page Sharing in Xen Virtual Machine Systems
1. - Myeongjae Jeon, Euiseong Seo, Junghyun Kim, Joonwon Lee
  - APPT, Nov. 2007 [PDF]

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