alt text 

Kai Wu
Ph.D.
Associate Professor
School of Artificial Intelligence
Xidian University, China

Main Building II-427
North Campus, Xidian University
No. 2 South Taibai Road, Xi’an, Shaanxi 710071

Email:kwu@xidian.edu.cn or kaiwu@stu.xidian.edu.cn [Google Scholar Profile]


Short Bio

Kai Wu received the B.S. degree in intelligent science and technology in 2015 from Xidian University, Xi’an, China, where he is currently working toward the Ph.D. degree in circuits and systems at the School of Artificial Intelligence. His current research interests include fuzzy cognitive maps, evolutionary computation, complex networks, and data mining.

He is the chair of IEEE Symposium 2020 on Multi-agent System Coordination and Optimization.

He is the chair of The 2nd International Conference on Industrial Artificial Intelligence, Invited Session: Industrial Artificial Intelligence for Time Series Data in Large-scale Complex Systems.

Research Topic

The main research interests are complex system modeling and its application, evolutionary optimization and learning.

  • 1. Complex system modeling and its application. We mainly use the designed model to learn and approximate some functions of complex system, and then use the learned model to finish various tasks.

    • 1.1. Complex system modeling based on fuzzy cognitive map. To achieve the effect of simulating the function of complex system, w can use the advantages of interpretability and abstraction of fuzzy cognitive map to learn the representation knowledge of complex system.

    • 1.2. Complex system modeling based on complex network. To achieve the purpose of controlling complex systemusing the dynamic characteristics of complex system, we can learn the network structure relationship of key factors in complex system according to the representation knowledge of complex system.

    • 1.3. Based on various applications of the model, time series prediction, time series classification and fault detection in complex system are mainly studied.

  • 2. Evolutionary optimization and learning. We solve optimization problems with complex structures, such as nonconvex, nondifferentiable and discontinuous problems with many local optimal solutions.

    • 2.1. Large scale optimization problem. Most of the practical problems have high-dimensional situation. Thus, we mainly study how to design evolutionary algorithm to solve the practical large-scale optimization problems.

    • 2.1. Expensive optimization problem. In most practical problems, the cost of objective function estimation is very high. We mainly study how to design evolutionary algorithm to solve the practical expensive optimization problem.

News

  • [Nov 2020] One paper is accepted by EMO2021

  • [Sep 2020] One paper is accepted by IEEE SSCI2020

  • [Sep 2020] One paper is accepted by IAI2020

  • [Sep 2020] One paper is accepted by IEEE Transactions on Cybernetics

  • [Sep 2020] I have joined in Xidian University

  • [Aug 2020] 2020-8-18, I have successfully defended my PhD dissertation and became a PhD

  • [Aug 2020] One paper is accepted by IEEE Transactions on Evolutionary Computation

  • [Aug 2020] One paper is accepted by Knowledge-based Systems

  • [May 2020] One paper is accepted by Knowledge-based Systems

  • [May 2020] One paper is accepted by IEEE Transactions on Fuzzy Systems

  • [Apr 2020] One paper is accepted by IEEE Transactions on Fuzzy Systems

  • [Feb 2020] One paper is accepted by IEEE Transactions on Fuzzy Systems

  • [Nov 2019] One paper is accepted by IEEE Transactions on Fuzzy Systems