I am an Assistant Professor in the Department of Electrical Engineering and Computer Science at DGIST. Previously, I was a postdoctoral researcher at the Siebel School of Computing and Data Science 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 data mining, query-aware data systems, streaming and adaptive computation, 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

Data Mining Query-Aware Data Systems Streaming & Adaptive Computation Applied AI

Selected Publications

Query-Aware Data Systems
Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries
Jun-Gi Jang and U Kang
KDD, 2021, Virtual Event  ·  Best Research Paper  ·  [paper]  ·  [homepage & code]

Large-Scale Data Mining
DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors
Jun-Gi Jang and U Kang
ICDE, 2022, Virtual Event  ·  Best Paper Honorable Mention  ·  [paper]  ·  [homepage & code]

Streaming & Adaptive Computation
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]

Query-Aware Data Systems
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]

Applied AI
Accurate Open-set Recognition for Memory Workload
Jun-Gi Jang, Sooyeon Shim, Vladimir Egay, Jeeyong Lee, Jongmin Park, Suhyun Chae, and U Kang
TKDD, 2023  ·  [paper]  ·  [code]

Positions

Sep. 2025 – Present
Assistant Professor, Electrical Engineering and Computer Science, DGIST
Aug. 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

Fellowships

  • Postdoctoral Fellowship Program, National Research Foundation of Korea. 2023 – 2024
  • Future Gauss Lecture Program, Gauss Labs. Feb. 2022
  • Naver Ph.D. Fellowship, Naver. Dec. 2021
  • Qualcomm Innovation Fellowship, Qualcomm. Nov. 2021
  • Yulchon AI Star Fellowship, Yulchon Foundation, Nongshim Group. Sep. 2021

Professional Service

Area Chair: KDD 2026

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

Full Publication List

Click to expand
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
Static and Streaming Tucker Decomposition for Dense Tensors
Jun-Gi Jang and U Kang
ACM Transactions on Knowledge Discovery from Data (TKDD), Oct., 2022. (Extended version of C2)
paper homepage & code bib
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 C3)
paper bib
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
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
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
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
Time-aware tensor decomposition for sparse tensors
Dawon Ahn, Jun-Gi Jang, and U Kang
Machine Learning, Sep. 2021.
paper code bib
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
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
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
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