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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. 11901575, Matrix Optimization Modelling for Tumor Heterogeneity Based on Single Cell Data, Young Scientists Fund, National Natural Science Foundation of China, 2020.01-2022.12
  2. 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
  3. 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
Publications
  1. Hao Jiang, Ming Yi, Shihua Zhang. A kernel non-negative matrix factorization framework for single cell clustering, Applied Mathematical Modelling, 90(2), 2020, 875-888.
  2. 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, 99(1), 2020.
  3. Xingheng Yu, Xinqi Gong*, Hao Jiang*. Heterogeneous multiple kernel learning for breast cancer outcome evaluation, BMC Bioinformatics, 21(3),2020,3.
  4. Hao Jiang, Yushan Qiu, Wenpin Hou, Xiaoqing Cheng, Man Yi Yim, and 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
  5. 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.
  6. Hao Jiang, Lydia L. Sohn, Haiyan Huang, and Luonan Chen. Single cell clustering based on cell-pair differentiability correlation and variance analysis, Bioinformatics, 34,2018, 3684-3694.
  7. 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.
  8. Yushan Qiu, Hao Jiang*, Wai-Ki Ching, Xiaoqing Cheng. Discovery of Boolean metabolic networks: integer linear programming based approach. BMC Systems Biology, 2018, 12.
  9. Hao Jiang, Yushan Qiu, Wai-Ki Ching et al. Hadamard kernel SVM with applications for breast cancer outcome predictions. BMC Systems Biology, 2017, 11.
  10. 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.
  11. Hao Jiang, Wai-Ki Ching, Yushan Qiu, Xiaoqing Cheng. Unconstrained optimization in projection method for indefinite SVMs, IEEE BIBM 2016, 2016.
  12. 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.
  13. Hao Jiang, Yushan Qiu, Xiaoqing Cheng, Wai-Ki Ching. On Eigen-matrix translation method for classification of biological data. Journal of Systems Science and Complexity, 2015, 28(5): 1212-1230
  14. 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
  15. 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
  16. Hao Jiang, Wai-Ki Ching, Kiyoko F.Aoki-Kinoshita and Dian-Jing Guo. Modeling genetic regulatory networks: A delay discrete dynamical model approach. Journal of System Science and Complexity, 2012, 25(6):1052-1067
  17. 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.

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