Yang Tang received the B.S. and Ph.D. degrees in electrical engineering from Donghua University, Shanghai, China, in 2006 and 2010, respectively. From 2008 to 2010, he was a Research Associate with The Hong Kong Polytechnic University, Hong Kong. From 2011 to 2015, he was a Post-Doctoral Researcher with the Humboldt University of Berlin, Berlin, Germany, and with the Potsdam Institute for Climate Impact Research, Potsdam, Germany. He is now a Professor with the East China University of Science and Technology, Shanghai. His current research interests include distributed estimation/control/optimization, cyber-physical systems, hybrid dynamical systems, computer vision, reinforcement learning and their applications.

Prof. Tang was a recipient of the Alexander von Humboldt Fellowship and has been the ISI Highly Cited Researchers Award by Clarivate Analytics from 2017. He has published over 100 refereed papers in international journals such as Nature Communications, Patterns (Cell Press), Automatica, IEEE Transactions, SIAM Journals and APS journals (including more than 20 papers in IEEE TAC and Automatica).  He is a Senior Board Member of Scientific Reports, an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Circuits and Systems-I: Regular Papers, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Systems Journal and Engineering Applications of Artificial Intelligence (IFAC Journal), etc. He is a leading guest editor for special issues in IEEE Transactions on Emerging Topics in Computational Intelligence and IEEE Transactions on Cognitive and Developmental Systems.

Department: Department of Automation, School of Information Science and Engineering

Address: No. 130 Meilong Road, Shanghai

Work Phone: 021-64256270

E-mail: yangtang@ecust.edu.cn

Research Interests:

Artificial Intelligence, Autonomous Systems, Computer Vision, Reinforcement Learning

Professional Services:

Senior Editorial Board Member:

• Scientific Reports (Springer Nature): 2019-

Editorial Board Member/Associate Editor:

• IEEE Transactions on Cybernetics (IEEE): 2022-

• IEEE Transactions on Circuits and Systems I: Regular Papers (IEEE): 2021-

• IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE): 2020-

• IEEE Transactions on Neural Networks and Learning Systems (IEEE): 2020-

• IEEE Systems Journal (IEEE): 2020-

• Engineering Applications of Artificial Intelligence (IFAC Journal, Elsevier): 2022-

Teaching:

Lectures in frontiers (2020- ), undergraduate course

Thesis writing (2019- ), graduate course

Research Program:

• National Key Research and Development Program of China (2021YFB1714300): Intelligent management and control of industrial big data-driven for product quality, (2021-2024), PI;

• Flaw detection of printer labels via artificial intelligence, Honeywell Company, (2021-2022) , PI.

• Program of Shanghai Academic Research Leader (20XD1401300): Estimation, Control and Optimization of Networked Coupled Dynamical Systems, (2020-2023) , PI;

• Chinesisch-Deutschen Zentrum für Wissenschaftsförderung (NSFC & DFG), Sino-German Mobility Programme (Sino-German Scientist Cooperation and Exchanges, M-0066): Distributed Control of Non-Holonomic Multi-Agent Systems in Wireless Communication Networks, (2020-2022) , PI;

• National Key Research and Development Program of China (2018YFC0809302): The construction techniques of knowledge graph for hazardous chemicals safety based on the fusion of data from diverse domains, (2018-2020) , PI;

• Renewed research from Alexander von Humboldt Research Stiftung, Germany, 2018: Formation control of multiple rigid bodies with switching topologies, 2018, PI;

• Natural Science Foundation of China (61751305): Risk early warning and smart decision making in the petroleum refinery process based on human-computer cooperative co-learning, (2018-2020) , PI;

• Natural Science Foundation of China (61673176): Coordination and control of hybrid multi-agent networks, (2017-2020) , PI.

Publications (Representative Works):

• Y. Tang, C. Zhao, J. Wang, C. Zhang, Q. Sun, W. Zheng, W. Du, F. Qian, and J. Kurths, An Overview of Perception and Decision-Making in Autonomous Systems in the Era of Learning, IEEE Transactions on Neural Networks and Learning Systems, accepted, 2022. (Survey Paper)

• Q. Sun, C. Zhao, Y. Tang, F. Qian, A Survey on Unsupervised Domain Adaptation in Computer Vision Tasks, SCIENTIA SINICA Technologica, accepted, 2022. (Survey Paper)

• J. Wang, Y. Hong, J. Wang, J. Xu, Y. Tang, Q. Han, and J. Kurths, Cooperative and Competitive Multi-agent Systems: From Optimization to Games, IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 5, pp. 763–783, May 2022. (Survey Paper, Featured Article)

• C. Zhang, J. Wang, G. Yen, C. Zhao, Q. Sun, Y. Tang, F. Qian, and J. Kurths, When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey, Patterns (Cell Press), vol. 1, no. 4, art. No. 100050, July 2020. (Survey Paper)

• Q. Sun, J. Fang, W. Zheng, Y. Tang, Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learning, IEEE Transactions on Industrial Electronics, accepted, 2022. (Regular Paper)

• Q. Sun, Y. Tang, C. Zhang, C. Zhao, F. Qian, and J. Kurths, Unsupervised Estimation of Monocular Depth and VO in Dynamic Environments via Hybrid Masks, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp. 2023-2033, May 2022. (Regular Paper)

• Y. Tang, X. Jin, Y. Shi, W. Du, Event-triggered Attitude Synchronization of Multiple Rigid Body Systems with Velocity-free Measurements, Automatica, accepted, 2022. (Regular Paper)

• X. Jin, Y. Shi, Y. Tang, H. Werner, J. Kurths, Event-triggered Fixed-time Attitude Consensus with Fixed and Switching Topologies, IEEE Transactions on Automatic Control, accepted, 2022.

• K. Chen, W. He, Q. Han, M. Xue, Y. Tang, Leader Selection in Networks with Switching Topologies and Antagonistic Interactions, Automatica, vol. 142, art. no 110334, Aug. 2022. (Regular Paper)

• X. Wu, Y. Tang, P. Shi, S. Mao, F. Qian, Stability Analysis of Semi-Markov Jump Stochastic Nonlinear Systems, IEEE Transactions on Automatic Control, vol. 67, no. 4, pp. 2084-2091, April 2022.

• J. Xu, G. Gu, Y. Tang, F. Qian, Channel Modeling and LQG Control in the Presence of Random Delays and Packet Drops, Automatica, vol. 135, art no. 109967, Jan. 2022. (Regular Paper)

• C. Zhao, G. Yen, Q. Sun, C. Zhang, Y. Tang, Masked GANs for Unsupervised Depth and Pose Prediction with Scale Consistency, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5392-5403, Dec. 2021. (Regular Paper)

• J. Xu, G. Gu, V. Gupta, Y. Tang, Optimal Stationary State Estimation Over Multiple Markovian Packet Drop Channels, Automatica, vol. 128, art. No. 109561, Jun. 2021.  (Regular Paper)

• Y. Tang, D. Zhang, P. Shi, W. Zhang, Feng Qian, Event-based formation control for nonlinear multiagent systems under DoS attacks, IEEE Transactions on Automatic Control, vol. 66, no. 1, pp. 452-459, Jan. 2021.

• X. Jin, Y. Shi, Y. Tang, X. Wu, Event-triggered attitude consensus with absolute and relative attitude measurements, Automatica, vol. 122, no. 12, art. No. 109245, Dec. 2020.

• J. Huang, D. W. C. Ho, F.i Li, W. Yang and Y. Tang, Secure Remote State Estimation against Linear Deception Attacks Using Watermarking, Automatica, vol. 121, art. No. 109182, Nov. 2020.

• M. Xue, Y. Tang, W. Ren, F. Qian, Practical output synchronization for asynchronously switched multi-agent systems with adaption to fast-switching perturbations, Automatica, vol. 116, art. No. 108917, Jun. 2020. (Regular Paper)

• Y. Tang, X. Wu, P. Shi, F. Qian, Input-to-State Stability for Nonlinear Systems with Stochastic Impulses, Automatica, vol. 113, no. 3, art no. 108766, Mar. 2020. (Regular Paper)

• J. Xu, Y. Tang, W. Yang, F. Li, L. Shi, Event-triggered Minimax State Estimation with a Relative Entropy Constraint, Automatica, vol. 110, no. 12, art. No. 108592, Dec. 2019. (Regular Paper)

• J. Xu, D. W. C. Ho, F. Li, W. Yang, Y. Tang, Event-triggered risk-sensitive state estimation for hidden Markov models, IEEE Transactions on Automatic Control, vol. 64, no. 10, pp. 4276-4283, Oct. 2019.