Jun-Gi Jang


jungi at illinois.edu
elnino9158 at gmail.com

Siebel School of Computing and Data Science
University of Illinois Urbana-Champaign

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Curriculum Vitae

I am a postdoctoral researcher working at Siebel School of Computing and Data Science at University of Illinois Urbana-Champaign (advisor: Prof. Hanghang Tong). My research interests include large-scale data mining, tensor mining, time series analysis. I received my Ph.D in Computer Science and Engineering at Seoul National University where I was advised by Prof. U Kang. I received B.S. in Mechanical & Aerospace Engineering at Seoul National University.

Research Interest

Large-scale Data Mining, Tensor Mining, Time series Analysis

Position

Postdoctoral Researcher in Siebel School of Computing and Data Science, UIUC. Aug. 2023 - Present

Work Experience

Postdoctoral Researcher in Computer Science and Engineering, SNU. March 2023 - Aug. 2023
Research Intern in Hyperconnect. July 2020 - Aug. 2020

Education

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

Awards

Best Thesis Award, CSE, SNU, Feb. 2023.
Best Paper Award, Honorable Mention, ICDE. 2022.
SNU BK21 Star Researcher Award, BK 21. Feb. 2022
BK21 Best Graduate Student Award, BK 21. Feb. 2022
Best Paper Award, Best Research Paper, KDD. 2021.
Best Paper Award, 1st Place, BigComp. 2021.
Humantech Paper Award, Honorable Mention, Samsung Electronics. Feb. 2018

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
Lecture/Research Scholarship, Seoul National University. Mar. 2019 - Aug. 2021

Publication

2024

C12. 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
Proceedings of the VLDB Endowment, Volume 17(13), 2024.

J11. 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]

C11. Fast and Accurate PARAFAC2 Decomposition for Time Range Queries on Irregular Tensors
Jun-Gi Jang, Yong-chan Park, and U Kang
ACM International Conference on Information and Knowledge Management (CIKM), 2024, Boise, Idaho, USA.
[paper] [code] [bib]

C10. Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition
Junghun Kim, Ka Hyun Park, Jun-Gi Jang, and U Kang
The 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024, Barcelona, Spain.
[paper] [code] [bib]

C9. 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
The 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024, Barcelona, Spain.
[paper] [code] [bib]

2023

C8. Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors - Algorithm and Application
Jun-Gi Jang, Jeongyoung Lee, Yong-chan Park, and U Kang
The 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023, Long Beach, CA, USA.
[paper] [code] [bib]

J10. 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, 2023.
[paper] [code] [bib]

J9. 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]

J8. 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]

J7. 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]

2022

C7. Accurate PARAFAC2 Decomposition for Temporal Irregular Tensors with Missing Values
Jun-Gi Jang, Jeongyoung Lee, Jiwon Park, and U Kang
IEEE International Conference on Big Data (BigData), 2022, Osaka, Japan.
[paper] [homepage & code] [bib] [slide]

C6. DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors
Jun-Gi Jang and U Kang
38th IEEE International Conference on Data Engineering (ICDE), 2022, Virtual Event.
[paper] [homepage & code] [bib] [slide]
Best Paper Award, Honorable Mention

J6. Static and Streaming Tucker Decomposition for Dense Tensors
Jun-Gi Jang and U Kang
ACM Transactions on Knowledge Discovery from Data, Oct., 2022.
It is the extended version of the conference paper C2.
[paper] [homepage & code] [bib]

J5. 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.
It is the extended version of the conference paper C3.
[paper] [bib]

J4. 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, Feb., 2022.
[paper] [bib]

2021

C5. Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries
Jun-Gi Jang and U Kang
The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021, Virtual Event.
[paper] [homepage & code] [bib] [slide]
Best Paper Award, Best Research Paper

C4. Fast and Accurate Partial Fourier Transform for Time Series Data
Yong-chan Park, Jun-Gi Jang, and U Kang
The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021, Virtual Event.
[paper] [code] [bib] [slide]

C3. VeST: Very Sparse Tucker Factorization of Large-Scale Tensors
Moonjeong Park*, Jun-Gi Jang* and Lee Sael (*equal contribution)
IEEE International Conference on Big Data and Smart Computing (BigComp), 2021, Jeju Island, Korea.
[paper] [homepage & code] [bib]
Best Paper Award, 1st Place

J3. Time-aware tensor decomposition for sparse tensors
Dawon Ahn, Jun-Gi Jang, and U Kang
Machine Learning, Sep. 27, 2021.
[paper] [code] [bib]

2020

C2. D-Tucker: Fast and Memory-Efficient Tucker Decomposition for Dense Tensors
Jun-Gi Jang and U Kang
36th IEEE International Conference on Data Engineering (ICDE) 2020, Dallas, Texas, USA
[paper] [homepage & code] [bib]

2019

J2. 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]

J1. 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. 1, 2019.
[paper] [code] [bib]

2018

C1. 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
ACM International Conference on Information and Knowledge Management (CIKM) 2018, Lingotto, Turin, Italy.
[paper] [homepage & code] [bib]

Thanks to Vasilios Mavroudis for the template