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赵杰
教授
数学学院
系统科学系
电话：
邮箱：
jie.zhao@seu.edu.cn
地址：
邮编：
  • 基本信息
  • 研究成果
  • 项目与荣誉
  • 社会兼职
  • 赵杰，教授，博士生导师，国家级高层次青年人才，江苏省应用数学科学研究中心固定成员。本科与硕士毕业于电子科技大学，后获 SUTD PhD Fellowship 全额资助，赴新加坡科技设计大学攻读博士学位，期间曾前往德国比勒菲尔德大学访问学习。博士毕业后，在新加坡南洋理工大学从事博士后研究工作。长期从事复杂网络、演化计算与计算智能等方向的研究，目前已发表学术论文30余篇，多篇论文入选 ESI 高被引论文。其中，以第一作者身份在 IEEE 汇刊及 CCF-A 类国际会议上发表论文10篇，包括 NeurIPS、IEEE Transactions on Evolutionary Computation (TEVC)、IEEE Transactions on Cybernetics (TCYB)、IEEE Transactions on Systems, Man, and Cybernetics (TSMC)、IEEE Transactions on Fuzzy Systems (TFS)、IEEE Transactions on Network Science and Engineering (TNSE) 等。曾在 IJCAI、IEEE SMC、ECAI 等国际会议上开设复杂网络与人工智能相关专题讲习班 (Tutorial)，并长期担任 ICLR、NeurIPS、ICML 以及 IEEE TMC、IEEE TDSC、IEEE TSMC 等国际顶级期刊与会议的审稿人。

    研究兴趣包括但不仅限于：
    1.面向图结构数据的人工智能方法（AI for Graph）
    2.基于图结构与关系建模的人工智能理论与方法（Graph for AI）
    3.面向复杂优化问题的人工智能方法（AI for Optimization）
    4.大模型智能体（LLM Agents）

    坚持以人为本的培养理念，将为学生提供系统的科研训练、持续的学术指导以及充分的科研与学术交流支持。期待与志同道合、热爱科研的学生在互相尊重的科研环境中共同成长进步。每年招收博士研究生1–2名、硕士研究生2–3名，欢迎提前联系咨询。

  • 代表作


    1. Jie Zhao and Kang Hao Cheong. Multi-Domain Evolutionary Optimization on Adversarial Link Perturbation in Imbalanced-Size Complex Systems. IEEE Transactions on Systems Man Cybernetics: Systems. DOI: 10.1109/TSMC.2026.3652853 


    2. Jie Zhao and Kang Hao Cheong. Structure-Aware Cooperative Ensemble Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models. The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025). 


    3. Jie Zhao, Kang Hao Cheong and Yaochu Jin. Multi-Domain Evolutionary Computation of Network Structure. IEEE Transactions on Cybernetics. DOI: 10.1109/TCYB.2025.3592664. 


    4. Jie Zhao and Kang Hao Cheong. Visual Evolutionary Optimization on Graph-Structured Combinatorial Problems with MLLMs: A Case Study of Influence Maximization. IEEE Transactions on Evolutionary Computation. DOI: 10.1109/TEVC.2025.3598266. 


    5. Jie Zhao, Tao Wen and Kang Hao Cheong. Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems? IEEE Transactions on Network Science and Engineering. DOI: 10.1109/TNSE.2025.3592367. 


    6. Jie Zhao and Kang Hao Cheong. Enhanced Epidemic Control: Community-Based Observer Placement and Source Tracing. IEEE Transactions on Systems Man Cybernetics: Systems 55(4), (2025): 2747-2758. 


    7. Jie Zhao and Kang Hao Cheong. MASE: Multi-Attribute Source Estimator for Epidemic Transmission in Complex Networks. IEEE Transactions on Systems Man Cybernetics: Systems 54(6), (2024): 3308-3320. 


    8. Jie Zhao and Kang Hao Cheong. Obfuscating community structure in complex network with evolutionary divide-and-conquer strategy. IEEE Transactions on Evolutionary Computation, 27(6), (2023): 1926-1940. 


    9. Jie Zhao, Zhen Wang, Jinde Cao and Kang Hao Cheong. A self-adaptive evolutionary deception framework for community structure. IEEE Transactions on Systems Man Cybernetics: Systems 53(8), (2023): 4954-4967. 


    10. Jie Zhao and Yong Deng. ”Complex network modeling of evidence theory.” IEEE Transactions on Fuzzy Systems 29(11), (2021): 3470-3480. 


    11. Jie Zhao and Kang Hao Cheong. ”Early identification of diffusion source in complex networks with evidence theory.” Information Sciences, 642 (2023): 119061. 


    12. Jie Zhao, Tao Wen, Hadi Jahanshahi and Kang Hao Cheong. The random walk-based gravity model to identify influential nodes in complex networks. Information Sciences, 609 (2022): 1706-1720.