杨绍富，副教授，硕士生导师，计算机科学与工程学院副院长。分别于 2010 年和 2013 年在东南大学数学学院获得学士和硕士学位，2016 年在香港中文大学机械与自动化工程系获得博士学位。随后，于 2016 年 6 月至 12 月，在香港城市大学电脑科学系任职博士后；2016 年 12 月至 2017 年 1 月，在中国科学院数学与系统科学研究所短期访问。2017 年 2 月起在东南大学计算机科学与工程学院任职副教授。
研究兴趣主要包括分布式优化、博弈、学习理论，及其在网络化系统（如无人机集群、智能电网、社会网络等）中的相关应用。作为项目负责人主持国家自然科学基金项目一项、江苏省自然科学基金项目一项。于 2018 年入选东南大学至善青年****、江苏省“双创博士”人才计划。
办公室：东南大学九龙湖校区计算机楼 532 室。邮箱：sfyang@seu.edu.cn.
每年招收2-3名硕士生。欢迎有科研热情的学生报考。同时欢迎本校有志于继续深造的本科生参与课题研究。

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News

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[2019-07]: Our paper on distributed self-triggered control for consensus is accepted in TSMCS.
[2019-04]: Our paper on consensus of heterogeneous agents is accepted in TAC.
[2019-02]: Our paper on pursuit-evasion problem is accepted in JIRS.
[2018-07]: Our paper on optimal resource allocation is accepted in TSMCS.
[2018-04]: Our paper on distributed minimax optimization is accepted as a full paper in TAC.
M.Sc. Opportunity:
We are looking for self-motivational master candidates in the areas of distributed optimization and learning, game theory, networked systems, etc. Send me your CV if you have interest to join our team.

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About me

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I am an associate professor with the School of Computer Science and Engineering at Southeast University. I received the B.Sc. and M.Sc. degrees in applied mathematics from the Department of Mathematics, Southeast University, Nanjing, China, in 2010 and 2013, respectively, and the Ph.D. degree from the Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong, in 2016, under the supervison of Prof. Jun Wang. I worked as a Post-Doctoral Fellow at the Department of Computer Science, City University of Hong Kong, Hong Kong, with Prof. Jun Wang, in 2016.
Contact Information:
Office: Room 532, Computer Building, Jiulonghu Campus of Southeast University, Nanjing 211189, China
Email: sfyang@seu.edu.cn

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Research Interests

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Distributed optimization
Network dynamics
Game theory
Computational intelligence
Learning theory

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Publications

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Journal papers:
[20] W. Xu, S. Yang, and J. Cao, Fully distributed self-triggered control for second-order consensus of multiagent systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, 2019.
[19] K. Li, Q. Liu, S. Yang, J. Cao, G. Lu, Cooperative optimization of dual multiagent system for optimal resource allocation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, 2019.
[18] K. Di, S. Yang, W. Wang, F. Yan, H. Xing, J. Jiang, Y. Jiang, Optimizing evasive strategies for an evader with imperfect vision capacity, Journal of Intelligent & Robotic Systems, 96: 419-437, 2019.
[17] S. Yang, J. Wang, Q. Liu, Consensus of nonlinear heterogeneous multiagent systems with duplex control laws, IEEE Transactions on Automatic Control, 64(12): 5140-5147, 2019.
[16] S. Yang, J. Wang, Q. Liu, Cooperative-competitive multi-agent systems for distributed minimax optimization subject to bounded constraints, IEEE Transactions on Automatic Control, 64(4): 1358-1372, 2019. (Full Paper)
[15] S. Gong, S. Yang, Z. Guo, T. Huang, Global exponentional synchronization of memristive competitive neural networks with time-varying delay via nonlinear control, Neural Processing Letters, 49(1): 103-119, 2019.
[14] Z. Guo, S. Gong, S. Yang, T. Huang, Global exponential synchronization of multiple coupled inertial memristive neural networks with time-varying delay via nonlinear coupling, Neural Networks, 108(12):260-271, 2018.
[13] S. Gong, S. Yang, Z. Guo, T. Huang, Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller, Neural Networks, 102(6):138-148, 2018.
[12] S. Yang, Q. Liu, J. Wang, A collaborative neurodynamic approach to multiple-objective distributed optimization, IEEE Transactions on Neural Networks and Learning Systems, 29(4):981-992, 2018.
[11] Q. Liu, S. Yang, Y. Hong, Constrained consensus algorithms with fixed step size for distributed convex optimization over multi-agent networks, IEEE Transactions on Automatic Control,62(8):4259-4265, 2017.
[10] Q. Liu, S. Yang, J. Wang, A collective neurodynamic approach to distributed constrained optimization, IEEE Transactions on Neural Networks and Learning Systems,28(8):1747-1758, 2017.
[9] S. Yang, Q. Liu, J. Wang, A multi-agent system with a proportional-integral protocol for distributed constrained optimization, IEEE Transactions on Automatic Control,62(7):3461-3467, 2017.
[8] S. Yang, Z. Guo, J. Wang, Global synchronization of multiple recurrent neural networks with time delays via impulsive interactions, IEEE Transactions on Neural Networks and Learning Systems,28(7):1657-1667, 2017.
[7] S. Yang, Q. Liu, J. Wang, Distributed optimization based on a multi-agent system in the presence of communication delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(5):717-728, 2017.
[6] Z. Guo, S. Yang, J. Wang, Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control, Neural Networks,84(12):67-79, 2016.
[5] Z. Guo, S. Yang, J. Wang, Global synchronization of stochastically disturbed memristive neurodynamics via discontinuous control laws, IEEE/CAA Journal of Automatica Sinica,3(2):121-131, 2016.
[4] X. Liu, M. Z. Q. Chen, H. Du, S. Yang, Further results on finite-time consensus of second-order multi-agent systems without velocity measurements, International Journal of Robust and Nonlinear Control, 26(14):3170-3185, 2016.
[3] S. Yang, Z. Guo, J. Wang, Robust synchronization of multiple memristive neural networks with uncertain parameters via nonlinear coupling, IEEE Transactions on Systems, Man, and Cybernetics: Systems,45(7):1077-1086, 2015.
[2] Z. Guo, S. Yang, J. Wang, Global exponential synchronization of multiple memristive neural networks with time delay via nonlinear coupling, IEEE Transactions on Neural Networks and Learning Systems,26(6):1300-1311, 2015.
[1] S. Yang, J. Cao, J. Lu, A new protocol for finite-time consensus of detail-balanced multi-agent networks, Chaos,22(4):043134, 2012.

Peer-reviewed conference papers:
[6] Q. Liu, J. Xiong, S. Yang, Mixed-norm projection-based iterative algorithm for face recognition, in the Proceeding of the 16th International Symposium on Neural Networks (ISNN), Moscow, Russia, pp. 331–340, 2019.
[5] W. Xu, S. Yang, Projection-based dynamics for distributed optimization subject to general constraints, in the Proceeding of the 37th Chinese Control Conference (CCC), Wuhan, China, pp. 2474-2478, 2018.
[4] S. Yang, W. Xu, Z. Guo, Distributed convergence to saddle-points over general directed multi-agent networks, in the Proceeding of the 14th International Conference on Control and Automation (ICCA),Anchorage, USA, pp. 538–543, 2018.
[3] S. Gu, T. Hao, S. Yang, The implementation of a pointer network model for traveling salesman problem on a Xilinx PYNQ board, in the Proceeding of 15th International Symposium on Neural Networks (ISNN),Minsk, Belarus, pp. 130-138, 2018.
[2] S. Yang, T. Huang, H. Li, W. Hu, Distributed saddle-point seeking via a continuous-time multi-agent system, in the Proceeding of the 14th International Workshop on Complex Systems and Networks (IWCSN),Doha, Qatar, pp. 290-295, 2017.
[1] S. Yang, J. Wang, Q. Liu, Multiple-objective optimization based on a two-time-scale neurodynamic system, in the Proceeding of the Eighth International Conference on Advanced Computational Intelligence (ICACI), Chiang Mai, Thailand, pp. 193-199, 2016.