Computer Science University of Illinois Urbana-Champaign
I am a postdoctoral researcher working at Computer Science at University of Illinois Urbana-Champaign (advisor: Prof. Hanghang Tong). My research interests include tensor analysis and 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.
Postdoctoral Researcher in Computer Science, UIUC. Aug. 2023 - Present
Postdoctoral Researcher in Computer Science and Engineering, SNU. March 2023 - Aug. 2023 Research Intern in Hyperconnect. July 2020 - Aug. 2020
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
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]
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]
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]
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]
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]
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]
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]