杨绍富

发布者:杨绍富发布时间:2019-03-19浏览次数:18705

杨绍富,副教授,博士生导师,计算机科学与工程学院、软件学院、人工智能学院副院长。中国人工智能学会机器学习专业委员会通讯委员。分别于 2010 和 2013 年在东南大学数学系获学士和硕士学位,导师为曹进德教授(IEEE Fellow);于 2016 年在香港中文大学机械与自动化工程系获博士学位,导师为王钧教授(IEEE Life Fellow)。曾先后访问香港城市大学计算机系、中国科学院数学与系统科学研究所。于 2017 年 2 月入职东南大学计算机科学与工程学院。主要从事分布式优化与博弈、强化学习、群体智能理论及应用研究。作为项目负责人主持国家自然科学基金项目 2 项、江苏省自然科学基金项目 1 项。于 2018 年入选东南大学至善青年学者、江苏省双创博士计划;于 2021 年入选中国科协青年人才托举工程、江苏省 333 高层次人才培养工程(第三层次)。

详细介绍请见:个人主页 (Personal Homepage)


联系方式

地址:江苏省南京市江宁区东南大学九龙湖校区计算机楼 532 室。

邮箱sfyang@seu.edu.cn

招生信息:每年招收5名左右硕士生,1名博士生。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。


在研项目

  1. 国家自然科学基金面上项目,2022.01-2025.12,57万,主持,在研。

  2. 中国科协青年人才托举工程项目,2022.01-2024.12,45万,主持,在研。


代表性论文

  1. Z. Zhang, S. Yang, W. Xu, and K. Di, Privacy-preserving distributed ADMM with event-triggered communication, IEEE Transactions on Neural Networks and Learning Systems, 2022, in press.          

  2. X. Wang, S. Yang, Z. Guo, S. Wen, and T. Huang, A distributed network system for nonsmooth coupled-constrained optimization, IEEE Transactions on Network Science and Engineering, 2022, in press.

  3. X. Wang, S. Yang, Z. Guo, M. Lian, T. Huang, A distributed dynamical system for optimal resource allocation over state-dependent networks, IEEE Transactions on Network Science and Engineering, 2022, in press.

  4. X. Wang, S. Yang, Z. Guo, and T. Huang, A second-order projected primal-dual dynamical system for distributed optimization and learning, IEEE Transactions on Neural Networks and Learning Systems, 2021, in press.

  5. 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, vol. 51, no. 6, pp. 3541–3551, 2021.

  6. S. Yang, J. Wang, and Q. Liu, Consensus of heterogeneous nonlinear multiagent systems with duplex control laws, IEEE Transactions on Automatic Control, vol. 64, no. 12, pp. 5140–5147, 2019.

  7. S. Yang, J. Wang, and Q. Liu, Cooperative-competitive multiagent systems for distributed minimax optimization subject to bounded constraints, IEEE Transactions on Automatic Control, vol. 64, no. 4, pp. 1358–1372, 2019. (Regular Paper)

  8. S. Yang, Q. Liu, and J. Wang, A collaborative neurodynamic approach to multiple-objective distributed optimization, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 981–992, 2018.

  9. S. Yang, Z. Guo, and J. Wang, Global synchronization of multiple recurrent neural networks with time delays via impulsive interactions, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 7, pp. 1657–1667, 2017.

  10. Q. Liu, S. Yang, and Y. Hong, Constrained consensus algorithms with fixed step size for distributed convex optimization over multiagent networks, IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 4259–4265, 2017.

  11. S. Yang, Q. Liu, and J. Wang, A multi-agent system with a proportional-integral protocol for distributed constrained optimization, IEEE Transactions on Automatic Control, vol. 62, no. 7, pp. 3461–3467, 2017. (ESI 高被引论文)

  12. Q. Liu, S. Yang, and J. Wang, A collective neurodynamic approach to distributed constrained optimization, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 8, pp. 1747–1758, 2017.

  13. S. Yang, Q. Liu, and J. Wang, Distributed optimization based on a multiagent system in the presence of communication delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 5, pp. 717–728, 2017. (ESI 高被引论文)

  14. Z. Guo, S. Yang, and J. Wang, Global exponential synchronization of multiple memristive neural networks with time delay via nonlinear coupling, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1300–1311, 2015.

  15. S. Yang, Z. Guo, and J. Wang, Robust synchronization of multiple memristive neural networks with uncertain parameters via nonlinear coupling, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 7, pp. 1077–1086, 2015. (ESI 高被引论文)


课程教学

  1. 自动控制原理(本科生,春季)

  2. 强化学习(本科生,春季)

  3. Optimization,Game,and Learning(研究生,秋季)