Welcome to Jie Chen's Homepage


Jie Chen

Jie Chen (陈杰) received the BSc degree in Software Engineering, MSc degree and PhD degree in Computer Science from Sichuan University, Chengdu, China, in 2005, 2008 and 2014, respectively. From Jul. 2008 to Dec. 2009, he was with Huawei Technologies Co., Ltd. as a software engineer. He was a Visiting Academic Researcher with the School of Computer Science and Informatics, De Montfort University, U.K., from Feb. 2019 to Feb. 2020, funded by China Scholarship Council. He is currently an Associate Professor with the College of Computer Science, Sichuan University, P. R. China. His current research interests include machine learning, big data analysis, and artificial intelligence.

Contact Information

  • Address: College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, P. R. China, 610065
  • Email: chenjie2010 [at] scu [dot] edu [dot] cn

Teaching (Apr. 2015-present)

  • 2015-presentCompilers - Principles, Techniques & Tools (编译原理), in Chinese and English, 3rd Year BSc Module, Team Member
  • 2021-present Design Patterns & Refactoring Practice (设计模式与重构实践), in Chinese, 3rd Year BSc Module, Module Leader
  • 2020-2023 Web Techniques & Practices (Web技术与实践), in Chinese, 3rd Year BSc Module, Module Leader

  • Funding (Selected Research Projects)

  • Multi-view Representation Learning from Incomplete Heterogeneous Multi-view Data in Dynamic Open Environments

    动态开放环境中的不完备异质多视图表示学习研究

    Source of Funds: National Natural Science Foundation of China

    Duration: 01/2026-12/2029

    Grant No.: 62576231

    Principal Investigator: Jie Chen

    Amount: CNY 500,000

  • Research on Intelligent Decision-Making System Towards Judicial Trials

    面向司法审判领域的智能决策系统研究

    Source of Funds: National Natural Science Foundation of China

    Duration: 01/2026-12/2029

    Grant No.: 62572331

    Co-Investigator: Jie Chen (Ranked Second)

    Amount: CNY 500,000 (CNY 200,000 allocated to Jie Chen)

  • Research on Open-Source Industrial Software Architecture

    开源工业软件架构研究

    Source of Funds: National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation

    Duration: 12/2024-12/2026

    Principal Investigator: Jie Chen

    Amount: CNY 300,000

  • Logical Analysis and Reconstruction of Open Source Code using Artificial Intelligence Methods

    开源代码逻辑分析与重构的人工智能方法

    Source of Funds: College of Aerospace Science and Engineering, National University of Defense Technology

    Duration: 06/2021-12/2023

    Grant No.: GF180711132

    Principal Investigator: Jie Chen

    Amount: CNY 1,500,000

  • Target Intelligent Recognition by Micro-neuro Columns and Deep Features Fusion Methods

    微神经柱与深度特征融合的智能目标识别方法

    Source of Funds: Sichuan Science and Technology Program

    Duration: 04/2021-03/2023

    Grant No.: 2021YJ0078

    Principal Investigator: Jie Chen

    Amount: CNY 150,000

  • Constructing a Family Member Relationship Prediction Model using Deep Neural Network Learning Methods

    面向深度神经网络学习的家庭成员人身关系预测模型构建

    Source of Funds: National Key Research and Development Program of China

    Duration: 11/2018-11/2021

    Grant No.: 2018YFC0831906

    Principal Investigator: Jie Chen (Sub-project)

    Amount: CNY 200,000

  • Low-rank Representation by Deep Neural Networks Methods

    低秩表示的深度神经网络方法

    Source of Funds: National Natural Science Foundation of China

    Duration: 01/2017-12/2019

    Grant No.: 61303015

    Principal Investigator: Jie Chen

    Amount: CNY 236,000

Publication (Selected Refereed Journal or Conference Papers)

[2025]

  • T. Zhu, H. Mao, H. Liu, and J. Chen*, Progressive low-confidence pseudolabeling for semisupervised node classification, Neurocomputing, vol. 656, Article 131166, Dec. 2025.
    [Paper] [Code & Datasets]
  • J. Chen, H. Mao, Y. Gou, and X. Peng*, Hierarchical sparse representation clustering for high-dimensional data streams, IEEE Trans. Neural. Netw. Learn. Syst., pp. 1-13, Jun. 2025, DOI: 10.1109/TNNLS.2025.3582380.
    [Paper] [Code & Datasets]
  • J. Chen, H. Mao, W. L. Woo, C. Liu, Z. Wang*, and X. Peng, One-step adaptive graph learning for incomplete multiview subspace clustering, IEEE Trans. Knowl. Data Eng., vol. 37, no. 5, pp. 2771-2783, May 2025.
    [Paper] [Code & Datasets]

[2024]

  • J. Chen, H. Mao, W. L. Woo, C. Liu*, and X. Peng, Cross-view graph consistency learning for invariant graph representations, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 15, 15795-15802, Apr. 2025.
    [Paper] [Code & Datasets]
  • J. Chen, Y. Chen, Z. Wang, H. Zhang, and X. Peng*, Spectral embedding fusion for incomplete multiview clustering, IEEE Trans. Image Process., vol. 33, pp. 4116-4130, Jul. 2024.
    [Paper] [Code & Datasets]

[2023]

  • J. Chen, S. Yang*, C. Fahy, Z. Wang, Y. Guo, and Y. Chen, Online sparse representation clustering for evolving data streams, IEEE Trans. Neural. Netw. Learn. Syst., vol. 36, no. 1, pp. 525-539, Jan. 2025.
    [Paper] [Code & Datasets][Supplementary material]
  • J. Chen, H. Mao*, D. Peng, C. Zhang, and X. Peng, Multiview clustering by consensus spectral rotation fusion, IEEE Trans. Image Process., vol. 32, pp. 5153-5166, Sept. 2023.
    [Paper] [Code & Datasets]
  • J. Chen, H. Mao, W. L. Woo, and X. Peng*, Deep multiview clustering by contrasting cluster assignments, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 16752-16761, Paris, France, Oct. 4-6, 2023.
    [Paper] [Code & Datasets][Supplementary material]

[2022]

  • J. Chen, Z. Wang*, H. Mao, and X. Peng, Low-rank tensor learning for incomplete multiview clustering, IEEE Trans. Knowl. Data Eng., vol. 35, no. 11, pp. 11556-11569, Nov. 2023.
    [Paper] [Code & Datasets]
  • J. Chen, Z. Wang*, S. Yang, and H. Mao, Two-stage sparse representation clustering for dynamic data streams, IEEE Trans. Cybern., vol. 53, no. 10, pp. 6408-6420, Oct. 2023.
    [Paper] [Code & Datasets][Supplementary material]
  • J. Chen, S. Yang*, and Z. Wang, Multi-view representation learning for data stream clustering, Inf. Sci., vol. 613, pp. 731-746, Oct. 2022.
    [Paper] [Code & Datasets]
  • J. Chen, S. Yang, X. Peng, D. Peng, and Z. Wang*, Augmented sparse representation for incomplete multiview clustering, IEEE Trans. Neural. Netw. Learn. Syst., vol. 35, no. 3, pp. 4058-4071, Mar. 2024.
    [Paper] [Code & Datasets]

[2021]

  • J. Chen, S. Yang*, Z. Wang, and H. Mao, Efficient sparse representation for learning with high-dimensional data, IEEE Trans. Neural. Netw. Learn. Syst., vol. 34, no. 8, pp. 4208-4222, Aug. 2023.
    [Paper] [Code & Datasets]
  • J. Chen, S. Yang*, H. Mao, and C. Fahy, Multiview subspace clustering using low-rank representation, IEEE Trans. Cybern., vol. 52, no. 11, pp. 12364-12378, Nov. 2022.
    [Paper] [Code & Datasets]
  • J. Chen, H. Mao*, Z. Wang, and X. Zhang, Low-rank representation with adaptive dictionary learning for subspace clustering, Knowl.-Based Syst., vol. 22, 107053, Jul. 2021.
    [Paper] [Code & Datasets]

[Before 2021]

  • J. Chen, H. Mao*, H. Zhang, and Z. Yi, Symmetric low-rank preserving projections for subspace learning, Neurocomputing, vol. 315, no.13, pp. 318-393, Nov. 2018.
    [Paper] [Code & Datasets]
  • J. Chen, H. Mao, Y. Sang, and Z. Yi*, Subspace clustering using a symmetric low-rank representation, Knowl.-Based Syst., vol. 127, no. 1, pp. 46-57, Jul. 2017.
    [Paper] [Code & Datasets]
  • J. Chen, H. Zhang, H. Mao, Y. Sang, and Z. Yi*, Symmetric low-rank representation for subspace clustering, Neurocomputing, vol. 173, no. 3, pp. 1192-1202, Jan. 2016.
    [Paper] [Code & Datasets]
  • J. Chen and Z. Yi*, Sparse representation for face recognition by discriminative low-rank matrix recovery, J. Vis. Commun. Image Represent., vol. 25, no. 5, pp. 763-773, Jul. 2014.
    [Paper] [Code][Datasets]

 

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