Education Experiences:
2005.09-2010.07, Ph.D. in Mathematics, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences
2001.09-2005.07, B.S. in Mathematics, School of Mathematics, Shandong University
Working Experiences:
2019.12-present, Professor, School of Mathematics, Renmin University of China
2020.10-2022.12, Head, Department of Information and Computation Sciences, School of Mathematics, RUC
2019.12-present, Principal Investigator, Eng. Res. Center of Finance Computation and Digital Engineering, Ministry of Education
2019.07-2019.08, Visiting Scholar, RMIT University, Australia
2018.01-2019.12, Professor, Beijing University of Chemical Technology
2012.06-2017.12, Associate Professor, Beijing University of Chemical Technology
2016.02-2017.02, Visiting Scholar, National University of Singapore, Singapore
2010.07-2012.05, Postdoctoral Fellow, Institute of Automation, CAS
Research Areas:
Iterative learning control, distributed artificial intelligence, stochastic control and optimization, stochastic approximation, decentralized/distributed control schemes, multi-agent systems
Courses:
Advanced Algebra I, II
Distributed Optimization
Iterative Learning Control
Selected Papers:
[1] Zihan Li, Dong Shen•, Xinghuo Yu. A Multistage Update Rule Framework for Iterative Learning Control Systems. IEEE Transactions on Automation Science and Engineering, 2024, accepted
[2] Zihan Li, Dong Shen•. Filter-Free Parameter Estimation Method for Continuous-Time Systems. IEEE Transactions on Automation Science and Engineering, 2024, accepted.
[3] 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.
[4] 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.
[5] Shuai Gao, Qijiang Song, Dong Shen•. Distributed Learning Control for High-Speed Trains with Operation Safety Constraints. IEEE Transactions on Cybernetics, vol. 54, no. 3, pp. 1794-1805, 2024.
[6] Shunhao Huang, Dong Shen•, JinRong Wang. Point-to-Point Learning Tracking Control via Fading Communication Using Reference Update Strategy. IEEE Transactions on Cybernetics, vol. 54, no. 4, pp. 2284-2294, 2024.
[7] Xiang Cheng, Hao Jiang, Dong Shen•. A Novel Accelerated Multistage Learning Control Mechanism via Virtual Performance Reduction. IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 5, pp. 6338-6352, 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] 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.
[10] Ganggui Qu, Dong Shen•, Qijiang Song, Xinghuo Yu. Point-to-Point Learning and Tracking for Networked Stochastic Systems With Fading Communications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 6, pp. 3600-3612, 2023.
[11] Dong Shen•. Practical Learning-Tracking Framework Under Unknown Nonrepetitive Channel Randomness. IEEE Transactions on Automatic Control, vol. 68, no. 6, pp. 3331-3347, 2023.
[12] Zihan Li, Dong Shen, Xinghuo Yu•. Enhancing Iterative Learning Control with Fractional Power Update Law. IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 5, pp. 1137-1149, 2023.
[13] Ganggui Qu, Dong Shen•, Xinghuo Yu. Batch-Based Learning Consensus of Multi-Agent Systems with Faded Neighborhood Information. IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 6, pp. 2965-2977, 2023.
[14] Dong Shen•, Niu Huo, Samer S. Saab. A Probabilistically Quantized Learning Control Framework for Networked Linear Systems. IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7559-7573, 2022.
[15] Dong Shen•, Samer S. Saab. Noisy-Output-Based Direct Learning Tracking Control with Markov Nonuniform Trial Lengths Using Adaptive Gains. IEEE Transactions on Automatic Control, vol. 67, no. 8, pp. 4123-4130, 2022.
[16] Dong Shen•, Ganggui Qu, Qijiang Song. Learning Control for Networked Stochastic Systems with Random Fading Communication. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 6, pp. 3659-3670, 2022.
[17] Dong Shen•, Chao Zhang. Zero-Error Tracking Control under Unified Quantized Iterative Learning Framework via Encoding-Decoding Method. IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 1979-1991, 2022.
[18] Dong Shen•, Xinghuo Yu. Learning Control over Unknown Fading Channels Based on Iterative Estimation. IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 1, pp. 48-60, 2022.
[19] Dong Shen•, Ganggui Qu, Xinghuo Yu. Averaging Techniques for Balancing Learning and Tracking Abilities Over Fading Channels. IEEE Transactions on Automatic Control, vol. 66, no. 6, pp. 2636-2651, 2021.
[20] Dong Shen•, Chen Liu, Lanjing Wang, Xinghuo Yu. Iterative Learning Tracking for Multisensor Systems: A Weighted Optimization Approach. IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1286-1299, 2021.
Books/ Monograph:
[1] Dong Shen. Variable Gain Design in Stochastic Iterative Learning Control. Springer, 2024.
[2] Dong Shen, Xinghuo Yu. Iterative Learning Control over Random Fading Channels. Taylor & Francis Group, CRC Press, 2024.
[3] Dong Shen, Xuefang Li. Iterative Learning Control for Systems with Iteration-Varying Trial Lengths: Synthesis and Analysis. Springer Singapore, 2019.
[4] Dong Shen. Iterative Learning Control with Passive Incomplete Information: Algorithm Design and Convergence Analysis. Springer Singapore, 2018.
[5] Shiping Yang, Jian-Xin Xu, Xuefang Li, Dong Shen. Iterative Learning Control for Multi-Agent Systems Coordination. Wiley & IEEE Press, 2017.
[6] Dong Shen. Stochastic Iterative Learning Control. Science Press, 2016. (in Chinese)
Services & Awards:
2024, The 17th Beijing Youth Outstanding Scientific and Technological Paper
2023, Top 2% among the most-cited scientists worldwide (by Stanford University)
2022, The National Young Talents Support Program
2022, Henan Provincial Natural Science Award (Second Prize, Place No. 2)
2022, IEEE 11th Data Driven Control and Learning Systems Conference Best Paper Award Finalist (Corr.Author)
2021, Outstanding Research Achievement Award of RUC (Paper Class)
2021, Shandong Provincial Natural Science Award (Second Prize, Place No. 3)
2021, The 12th Teaching Competition for Young Teachers in Beijing Universities (Second Prize)
2020, Outstanding Research Achievement Award of RUC (Paper Class)
2014, IEEE CSS Beijing Chapter Young Author Prize
2012, Wentsun Wu Artificial Intelligence S&T Progress Award (Second Prize, Place No. 5)
