I am an Assistant Professor in the Graduate School of Data Science at Seoul National University, with a joint appointment at SNU College, where I lead the D³ Lab. Before joining SNU, I was an Assistant Professor at DGIST and a postdoctoral researcher at the University of Illinois Urbana-Champaign, where I worked with Prof. Hanghang Tong. I received my Ph.D. in Computer Science and Engineering from Seoul National University, where I was advised by Prof. U Kang. I also received my B.S. in Mechanical and Aerospace Engineering from Seoul National University, with a double major in Computer Science and Engineering. My research focuses on scalable data mining systems, model efficiency and compression, and applied AI. I have received best paper awards, including the Best Research Paper Award at KDD 2021 and the Best Paper Award Honorable Mention at ICDE 2022.

Research Interests

Scalable Data Mining Systems Model Efficiency & Compression Applied AI

Prospective Students

I am actively looking for motivated students and researchers to join my group at SNU GSDS — undergraduate research interns, M.S., M.S.–Ph.D. integrated, Ph.D., and postdoctoral researchers.

Our group develops scalable systems for querying and mining complex, evolving data, and explores model efficiency and compression. Research topics include, but are not limited to:

Scalable Data Mining Systems Model Efficiency and Compression AI + X

Interested in one of these topics?

See specific research directions, related papers, and how to apply → [Link].

Please email me at jungijang at snu.ac.kr with your CV, transcript, a short statement of interest, and links to relevant projects or research work, if available. Please review the research topics and recent publications at jungijang.github.io and the D³ Lab website before contacting us.

Positions

Sep. 2026 – Present
Assistant Professor, Graduate School of Data Science, SNU
Joint Appointment, SNU College
Sep. 2025 – Aug. 2026
Assistant Professor, Electrical Engineering and Computer Science, DGIST
Sep. 2023 – Aug. 2025
Postdoctoral Researcher, Siebel School of Computing and Data Science, UIUC
Mar. 2023 – Aug. 2023
Postdoctoral Researcher, Computer Science and Engineering, SNU
Jul. 2020 – Aug. 2020
Research Intern, Hyperconnect

Education

Mar. 2017 – Feb. 2023
Seoul National University
Ph.D. in Computer Science and Engineering
Mar. 2010 – Feb. 2017
Seoul National University
BSc in Mechanical & Aerospace Engineering
(Double Major: Computer Science and Engineering)

Awards & Honors

  • Qualcomm Innovation Fellowship, Qualcomm. Nov. 2021
  • Naver Ph.D. Fellowship, Naver. Dec. 2021
  • Postdoctoral Fellowship Program, National Research Foundation of Korea. 2023 – 2024
  • Yulchon AI Star Fellowship, Yulchon Foundation, Nongshim Group. Sep. 2021
  • Future Gauss Lecture Program, Gauss Labs. Feb. 2022
  • Best Thesis Award, CSE, SNU. Feb. 2023
  • SNU BK21 Star Researcher Award, BK 21. Feb. 2022
  • BK21 Best Graduate Student Award, BK 21. Feb. 2022

Professional Service

Area Chair: KDD 2026, 2027

Program Committee / Reviewer: KDD, WWW, CIKM, SDM, AAAI, TKDE, KAIS, TPDS, and others

Workshop Organizing Committees: Interplay Between Classical Tensor Methods And Foundation Models, 2026

Publications

[28] SCOUT: Coupling-Free Bounds for Trillion-Scale Top-k Retrieval in Sparse Tensor Factorization
Jun-Gi Jang, Hyunsik Yoo, Ruizhong Qiu, Yinglong Xia, and Hanghang Tong
SIGMOD, 2027, Huntington Beach, CA, USA.
paper
[27] DuET: Dual-View Tensor-to-Topology Spectral Adapter for Enhancing Sparse Tensor Factorization
Jun-Gi Jang, Jingrui He, Andrew J Margenot, Hanghang Tong
KDD, 2026, Jeju, South Korea.
paper
[26] ProgNet: Program-Grounded Evidence Composition for Interpretable Graph Classification
Minseok Jeon, Seunghyun Park, Jun-Gi Jang (Corresponding)
KDD, 2026, Jeju, South Korea.
paper
[25] Fast and Accurate Domain Adaptation for Irregular and Regular Tensor Decomposition
Junghun Kim, Ka Hyun Park, Jun-Gi Jang, and U Kang
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2026.
paper
[24] Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation
Dawon Ahn*, Jun-Gi Jang*, Evangelos E. Papalexakis (*equal contribution)
PAKDD, 2025, Sydney, Australia.
paper code
[23] TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Ranges
Ruizhong Qiu*, Jun-Gi Jang*, Xiao Lin, Lihui Liu, Hanghang Tong (*equal contribution)
VLDB 2025, London, United Kingdom.
paper code
[22] Compact Lossy Compression of Tensors via Neural Tensor-Train Decomposition
Taehyung Kwon, Jihoon Ko, Jinhong Jung, Jun-Gi Jang, and Kijung Shin
Knowledge and Information Systems (KAIS), Springer, 2024.
paper code bib
[21] Fast and Accurate PARAFAC2 Decomposition for Time Range Queries on Irregular Tensors
Jun-Gi Jang, Yong-chan Park, and U Kang
CIKM, 2024, Boise, Idaho, USA.
paper code bib
[20] Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition
Junghun Kim, Ka Hyun Park, Jun-Gi Jang, and U Kang
KDD, 2024, Barcelona, Spain.
paper code bib
[19] Compact Decomposition of Irregular Tensors for Data Compression: From Sparse to Dense to High-Order Tensors
Taehyung Kwon, Jihoon Ko, Jinhong Jung, Jun-Gi Jang, and Kijung Shin
KDD, 2024, Barcelona, Spain.
paper code bib
Show 18 earlier publications (2018–2023)
[18] Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors - Algorithm and Application
Jun-Gi Jang, Jeongyoung Lee, Yong-chan Park, and U Kang
KDD, 2023, Long Beach, CA, USA.
paper code bib
[17] Accurate Open-set Recognition for Memory Workload
Jun-Gi Jang, Sooyeon Shim, Vladimir Egay, Jeeyong Lee, Jongmin Park, Suhyun Chae, and U Kang
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023.
paper code bib
[16] Fast and accurate interpretation of workload classification model
Sooyeon Shim, Doyeon Kim, Jun-Gi Jang, Suhyun Chae, Jeeyong Lee, and U Kang
PLOS ONE, 2023.
paper code bib
[15] Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters
Hyunsik Jeon, Jun-Gi Jang, Taehun Kim, and U Kang
PLOS ONE, 2023.
paper code bib
[14] Falcon: lightweight and accurate convolution based on depthwise separable convolution
Jun-Gi Jang*, Chun Quan*, Hyun Dong Lee, and U Kang (*equal contribution)
Knowledge and Information Systems (KAIS), Springer, 2023.
paper code bib
[13] Accurate PARAFAC2 Decomposition for Temporal Irregular Tensors with Missing Values
Jun-Gi Jang, Jeongyoung Lee, Jiwon Park, and U Kang
IEEE BigData, 2022, Osaka, Japan.
paper homepage & code bib slide
[12] DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors
Jun-Gi Jang and U Kang
ICDE, 2022, Virtual Event.   Best Paper Award, Honorable Mention
paper homepage & code bib slide
[11] Static and Streaming Tucker Decomposition for Dense Tensors
Jun-Gi Jang and U Kang
ACM Transactions on Knowledge Discovery from Data (TKDD), Feb., 2023. (Extended version of [4])
paper homepage & code bib
[10] Large-scale Tucker Tensor Factorization for Sparse and Accurate Decomposition
Jun-Gi Jang*, Moonjeong Park*, Jongwuk Lee, and Lee Sael (*equal contribution)
The Journal of Supercomputing, May, 2022. (Extended version of [6])
paper bib
[9] Finding Key Structures in MMORPG Graph with Hierarchical Graph Summarization
Jun-Gi Jang, Chaeheum Park, Changwon Jang, Geonsoo Kim, and U Kang
ACM Transactions on Knowledge Discovery from Data (TKDD), Feb., 2022.
paper bib
[8] Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries
Jun-Gi Jang and U Kang
KDD, 2021, Virtual Event.   Best Paper Award, Best Research Paper
paper homepage & code bib slide
[7] Fast and Accurate Partial Fourier Transform for Time Series Data
Yong-chan Park, Jun-Gi Jang, and U Kang
KDD, 2021, Virtual Event.
paper code bib slide
[6] VeST: Very Sparse Tucker Factorization of Large-Scale Tensors
Moonjeong Park*, Jun-Gi Jang* and Lee Sael (*equal contribution)
BigComp, 2021, Jeju Island, Korea.   Best Paper Award, 1st Place
paper homepage & code bib
[5] Time-aware tensor decomposition for sparse tensors
Dawon Ahn, Jun-Gi Jang, and U Kang
Machine Learning, Sep. 2021.
paper code bib
[4] D-Tucker: Fast and Memory-Efficient Tucker Decomposition for Dense Tensors
Jun-Gi Jang and U Kang
ICDE, 2020, Dallas, Texas, USA.
paper homepage & code bib
[3] S3CMTF: Fast, accurate, and scalable method for incomplete coupled matrix-tensor factorization
Dongjin Choi, Jun-Gi Jang, and U Kang
PLOS ONE, 2019.
paper homepage & code bib
[2] High-Performance Tucker Factorization on Heterogeneous Platforms
Sejoon Oh, Namyong Park, Jun-Gi Jang, Lee Sael, and U Kang
IEEE Transactions on Parallel and Distributed Systems (TPDS), Apr. 2019.
paper code bib
[1] Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range
Jun-Gi Jang, Dongjin Choi, Jinhong Jung, and U Kang
CIKM, 2018, Turin, Italy.
paper homepage & code bib