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 Prof. SHEN's Group
Distributed Artificial Intelligence Laboratory, ERC-FCDE, MoE
School of Mathematics, Renmin University of China
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DongShen
Research Interests: Machine learning and its applications, iterative learning control, distributed and decentralized optimization algorithms.

Contact Information
Office Address:
Room 327, Information Building, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing 100872
Mailing Address:
School of Mathematics, Renmin University of China, No. 59 Zhongguancun Street, Beijing 100872, P.R. China
Tel: 86-10-82500688
E-mail: jiangh [at] ruc.edu.cn

Education

2009.09-2013.07, Ph.D. in Mathematics, Department of Mathematics, the University of Hong Kong
2005.09-2009.07, B.S. in Mathematics, School of Mathematics, Harbin Institute of Technology

Professional Positions

2024.08-present, Professor, School of Mathematics, Renmin University of China
2019.08-2024.07, Associate Professor, School of Mathematics, Renmin University of China
2018.06-2019.08, Assistant Professor, School of Mathematics, Renmin University of China
2013.08-2018.06, Assistant Professor, School of Information, Renmin University of China
2011.06-2011.08, Visiting Scholar, Kyoto University, Japan
2010.05-2010.08, Visiting Scholar, Soka University, Japan

Research Fundings
  1. 12271522, Optimal Integration Modeling and Heterogeneity Analysis of Single Cell Multi-Omics Data, National Natural Science Foundation of China, 2023.01-2026.12
  2. Drug Target Discovery Based on Multimodal Data Fusion, Joint Research Project between Mathematics and Artificial Intelligence Application Enterprise, 2025.01-2025.12
  3. 11901575, Matrix Optimization Modelling for Tumor Heterogeneity Based on Single Cell Data, National Natural Science Foundation of China, 2020.01-2022.12
  4. 91730301, Computational Modeling of Stem Cell Proliferation and Its Application to the Dynamics of Cancer Evolution, Major Research Plan Integration Project, National Natural Science Foundation of China, 2018.01-2019.12
  5. 11626229, Research on Fast Credit Evaluation System Based on Rank-Deficient Kernel Support Vector Machines, Tianyuan Youth Fund Project, National Natural Science Foundation of China, 2017.01-2017.12
Journal Publications

  1. Senwen Zhan, Hao Jiang, Dong Shen, Wai-Ki Ching. Multi-View Data Clustering via Dynamical Optimization of Consensus Laplacian Matrix. East Asian Journal on Applied Mathematics, 16(1), 20-44, 2026.
  2. Senwen Zhan, Hao Jiang, Dong Shen. Co-regularized optimal high-order graph embedding for multi-view clustering. Pattern Recognition, 157, 110892, 2025.
  3. Yixiang Huang, Hao Jiang, Wai-Ki Ching, Dong Shen. scRECL: representative ensembles with contrastive learning for scRNA-seq data clustering analysis. Briefings in Bioinformatics, 26(4), bbaf346, 2025.
  4. Shuai Gao, Qijiang Song, Hao Jiang, Dong Shen, Yisheng Lv. Decentralized learning control for high-speed trains with unknown time-varying speed delays. Applied Mathematical Modelling, 137, Part B, 2025, 115711.
  5. Yixiang Huang, Hao Jiang, Wai-Ki Ching. scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data. Briefings in Bioinformatics, Volume 25, Issue 3, bbae203, May 2024.
  6. Yushan Qiu, Lingfei Yang, Hao Jiang, Quan Zou. scTPC: A novel semi-supervised deep clustering model for scRNA-seq data. Bioinformatics, 40(4), 2024, btae293.
  7. Shuai Gao, Qijiang Song, Hao Jiang, Dong Shen. History Makes Future: Iterative Learning Control for High-Speed Trains. IEEE Intelligent Transportation Systems Magazine, vol. 16, no. 1, pp. 6-21, 2024.
  8. Hao Jiang, Dong Shen, Shunhao Huang, Xinghuo Yu. Accelerated Learning Control for Point-to-Point Tracking Systems. IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 1265-1277, 2024.
  9. Zeyi Zhang, Hao Jiang, Dong Shen, Samer S. Saab. Data-driven Learning Control Algorithms Meeting Unachievable Tracking Problems. IEEE/CAA Journal of Automatica Sinica, vol. 11, no. 1, pp. 205-218, 2024.
  10. Hao Jiang, Senwen Zhan, Wai-Ki Ching, Luonan Chen. Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood laplacian matrix optimization. Bioinformatics, 39(7), btad414, 2023.
  11. Hao Jiang, Xun He, Qijiang Song, Dong Shen. Decentralized Learning Control for LargeScale Systems with Gain Adaptation Mechanism. Information Sciences, 623, 539-558, 2023.
  12. Hao Jiang, Jing xin Liu, You Song, Jinzhi Lei. Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method. Journal of Computational Biology, 2023.
  13. X. Cheng, C. Yan, Hao Jiang, Y. Qiu. scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy. IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 2, pp. 1431-1444, 1 March-April 2023.
  14. Xiang Cheng, Hao Jiang, Dong Shen, Xinghuo Yu. A Novel Adaptive Gain Strategy for Stochastic Learning Control. IEEE Transactions on Cybernetics, vol. 53, no. 8, pp. 5264-5275, 2023.
  15. Hao Jiang, Dong Shen, Wai-Ki Ching, Yushan Qiu. A High-Order Norm-Product Regularized Multiple Kernel Learning Framework for Kernel Optimization. Information Sciences, 606, 72-91, 2022.
  16. Niu Huo, Hao Jiang, Dong Shen, JinRong Wang. Finite-Level Uniformly Quantized Learning Control with Random Data Dropouts. International Journal of Robust and Nonlinear Control, 33(7), 4056-4075, 2023.
  17. Hao Jiang, Ming Yi, Shihua Zhang. A kernel non-negative matrix factorization framework for single cell clustering. Applied Mathematical Modelling, 90(2), 2021, 875-888.
  18. Yushan Qiu, Hao Jiang, Wai-Ki Ching. Unsupervised learning framework with multidimensional scaling in predicting epithelial-mesenchymal transitions. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2021, 2714-2723.
  19. Xingheng Yu, Xinqi Gong, Hao Jiang. Heterogeneous multiple kernel learning for breast cancer outcome evaluation. BMC Bioinformatics, 21, 2020, 155.
  20. Hao Jiang, Yushan Qiu, Wenpin Hou, Xiaoqing Cheng, Man Yi Yim, Wai-Ki Ching. Drug Side-effect Profiles Prediction: From Empirical Risk Minimization to Structural Risk Minimization. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 17(2), 2020, 402-410.
  21. Yushan Qiu, Hao Jiang, Wai-Ki Ching, Michael K.Ng. On predicting mesenchymal transition by integrating RNA binding proteins and correlation data via L1/2-regularization method. Artificial Intelligence in Medicine, 95, 2019.
  22. Hao Jiang, Lydia L. Sohn, Haiyan Huang, Luonan Chen. Single cell clustering based on cell-pair differentiability correlation and variance analysis. Bioinformatics, 34, 2018, 3684-3694.
  23. Hao Jiang, Wai Ki Ching, Ka Fai Cedric Yiu, Yushan Qiu. Stationary Mahalanobis kernel SVM for credit risk evaluation. Applied Soft Computing, 71, 2018, 407-417.
  24. Yushan Qiu, Hao Jiang, Wai-Ki Ching, Xiaoqing Cheng. Discovery of Boolean metabolic networks: integer linear programming based approach. BMC Systems Biology, 2018, 12.
  25. Hao Jiang, Yushan Qiu, Wai-Ki Ching et al. Hadamard kernel SVM with applications for breast cancer outcome predictions. BMC Systems Biology, 2017, 11.
  26. Hao Jiang, Yushan Qiu, Wai-Ki Ching et al. Optimal Projection method determination by Logdet Divergence and Perturbed von Neumann Divergence. BMC Systems Biology, 2017, 11.
  27. Hao Jiang, Wai-Ki Ching, Wenpin Hou. On Orthogonal Feature Extraction model with applications in medical prognosis. Applied Mathematical Modelling, 2016,40(19-20):8766-8776
  28. Hao Jiang, Yushan Qiu, Xiaoqing Cheng, Wai-Ki Ching. On Eigen-matrix translation method for classification of biological data. Journal of Systems Science & Complexity, 2015, 28(5):1212-1230
  29. Hao Jiang, Takeyuki Tamura, Wai-Ki Ching, Tatsuya Akutsu. On the complexity of inference and completion of Boolean networks from given singleton attractors. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2013, E96. A(11):2265-2274
  30. Hao Jiang, Wai-Ki Ching. Correlation kernels for support vector machines classification with applications in cancer data. Computational and Mathematical Methods in Medicine, 2012, v. 2012 article no. 205025
  31. Hao Jiang, Wai-Ki Ching, Kiyoko F. Aoki-Kinoshita, Dian-Jing Guo. Modeling genetic regulatory networks: A delay discrete dynamical model approach. Journal of System Science & Complexity, 2012, 25(6):1052-1067
  32. Hao Jiang, Xi Chen, Wai-Ki Ching. On Generating Optimal Sparse Probabilistic Boolean Networks with Maximum Entropy from a Positive Stationary Distribution. East Asian Journal on Applied Mathematics, 2012, 2(4):353-372.
  33. X. Chen, Hao Jiang, Y. Qiu, W Ching, On Optimal Control Policy for Probabilistic Boolean Network: A State Reduction Approach. BMC Systems Biology, 2012.
  34. Hao Jiang, Wai-Ki Ching, Delin Chu. Discriminant analysis in pairwise kernel learning for SVM classification. International Journal of Bioinformatics Research and Applications, 2012.
  35. X. Chen, Hao Jiang, W. Ching, On Construction of Sparse Probabilistic Boolean Networks. East Asian Journal of Applied Mathematics, 2 (2012) 1-18.
  36. Hao Jiang, Wai-Ki Ching, Kiyoko F Aoki-Kinoshita. Extracting Glycan Motifs using a Biochemically-Weighted Kernel.  Bioinformation, 7(5) (2011) 405-412.
  37. Hao Jiang, Wai-Ki Ching, Classifying DNA repair genes by kernel-based support vector machines. Bioinformation, 7(8) (2011) 257-263.
  38. Hao Jiang, Wai-Ki Ching, Zeyu Zheng. Kernel Techniques in Support Vector Machines for Classification of Biological Data. International Journal of Information Technology and Computer Science, 3(2) (2011) 1-8.
  39. Xi Chen, Hao Jiang, Wai-Ki Ching, Limin, Li. On Construction of Gene-PDB Structure Mapping with Applications in Functional Annotation of Human Genes. International Journal of Information Technology and Computer Science, 3(2) (2011) 53-59.
  40. Wai-Ki Ching, Ho-Yin Leung, Hao Jiang, Liang Sun, Tak-Kuen Siu, A Markovian Network Model for Default Risk Management. International Journal of Intelligent Engineering Informatics, 1(1) (2010) 104-124.
Conference Papers
  1. Xun He, Hao Jiang, Dong Shen. Iterative Learning Control for Linear Systems With Random Actuator Faults. The 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS'23), Xiangtan, China, 12-14 May, 2023, pp. 1556-1561.
  2. Jiaxi Qiang, Hao Jiang, Dong Shen. Iterative Learning Control Based on First-order Accelerated Gradient Method. The 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS'23), Xiangtan, China, 12-14 May, 2023, pp. 1544-1549.
  3. Zeyi Zhang, Hao Jiang, Kun Zeng, Dong Shen. Collaborative Learning Tracking for Networked Systems With Fading Communication. The 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS'23), Xiangtan, China, 12-14 May, 2023, pp. 728-732.
  4. Xingying Zhao, Hao Jiang, Dong Shen. EOGFACE: Deep Face Recognition Via Extensional Logits. 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 16-19 October, 2022, pp. 311-315.
  5. Xun He, Hao Jiang, Dong Shen. Iterative Learning Control for Multi-Agent Systems Over Unknown Fading Networks. The IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS'22), Emeishan, China, 13-15 May, 2022, pp. 976-981.
  6. Zeyi Zhang, Hao Jiang, Dong Shen. Extended Iterative Learning Control for Inconsistent Tracking Problems with Random Dropouts. The IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS'22), Emeishan, China, 13-15 May, 2022, pp. 935-940. (This paper won IEEE 11th DDCLS Best Paper Award Finalist)
  7. Yiyao Dou, Hao Jiang. Informative gene identification for Single-Cell RNA-Seq Data with Mutual Information based Firefly Algorithm, ICBBE2021.
  8. Xiaoqing Cheng, Chang Yan, Hao Jiang, Yushan Qiu, HOMC: A Hierarchical Clustering Algorithm Based on Optimal Low Rank Matrix Completion for Single Cell Analysis, D.-S. Huang et al. (Eds.): ICIC 2021, LNAI 12838, 66–76, 2021.
  9. Hao Jiang, Wai-Ki Ching, Yushan Qiu, XiaoQing-Chen. Unconstrained optimization in projection method for indefinite SVMs. IEEE International Conference on Bioinformatics and Biomedicine(BIBM2016), Dec 15-18, Shenzhen, China, 584-591.
  10. Hao Jiang, Yushan Qiu, Xiaoqing Cheng, Wai-Ki Ching. A Parsimonious Model for Predicting Drug Side-effect Profiles. InCoB 2014, July 31- Aug 2, 2014, Melbourne, Australia.
  11. Limin Li, Hao Jiang, Wai-Ki Ching and Vassilis S. Vassiliadis. Metabolite Biomarker Discovery For Metabolic Diseases By Flux Analysis. IEEE ISB2012, Aug 18-20, 2012, Xi'an, China.
  12. Hao Jiang, Wai-Ki Ching, Delin Chu. Discriminant analysis in pairwise kernel learning for SVM classification. APBC2012, Jan 15-19, 2012, Melbourne, Australia.
  13. Hao Jiang and Wai-Ki Ching. Physico-Chemically Weighted Kernel for SVM Protein Classification. Proceedings of the 2nd international Conference on Biomedical Engineering and Computational Science(ICBECS2011), Apr 23-24, 2011, Wuhan, China.
  14. Xi Chen, Hao Jiang, Wai-Ki Ching, Limin Li. Inferring Functional Annotation for Human Genes from Gene-PDB Structure Mapping. Proceedings of the 2nd international Conference on Biomedical Engineering and Computational Science(ICBECS2011), Apr 23-24,2011, Wuhan, China.
  15. Hao Jiang, K. Aoki-Kinoshita, Wai-Ki Ching. Extracting Glycan Motifs using a Biochemically Weighted Kernel InCoB/ISCB-Asia 2011 Conference Supplement Issue, Nov 30-Dec 2, 2011, Kuala Lumper, Malaysia.
  16. Hao Jiang, Wai-Ki Ching, Kiyoko F. Aoki-Kinoshita, DianJing Guo. Delay Discrete Dynamical Models for Genetic Regulatory Networks. The 4th International Conference, ISB2010, Suzhou, China, Sep 9-11, 2010 Proceedings.
  17. Wai-Ki Ching, Ho-Yin Leung, Hao Jiang, Liang Sun. A Markovian network Model for default risk management. Proceedings of the 2nd International Symposium on Financial Information Processing. Beijing, China, May 29-31, 2009.

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