Professional Title / Position：Professor, Doctoral Supervisor


Research Direction：High-performance AI algorithm design and cross-domain applications, with core focuses on: Intelligent optimization and decision-making for complex systems, Multimodal large Language models, and Explainable AI for Science


Team：Computational Intelligence


Email：jinghuizhong@scut.edu.cn


 Biography：

Dr. Jinghui Zhong is a Young Top-notch Scholar under Guangdong’s High-Level Talent Program. His research specializes in high-performance AI algorithms and their interdisciplinary applications. He has published 100+ papers in internationally renowned journals and conferences, including 40+ IEEE/ACM Transactions papers, and has been consistently ranked among Stanford’s Top 2% Global Scientists. He currently serves as:  Editorial Board Member of Memetic Computing, ICT Express, Senior Member of IEEE and CCF, Vice Chair of IEEE Computational Intelligence Society (CIS) Guangzhou Chapter. Dr. Zhong has led 20+ national/provincial research projects and industry collaborations.


 Education：

2009–2012 Sun Yat-sen University, Ph.D.,2005–2007 Sun Yat-sen University, M.S.,2001–2005 Sun Yat-sen University, B.S.,


 Work Experience：

2016–Present Associate Professor → Professor, South China University of Technology (SCUT)2013–2016 Postdoctoral Researcher, Nanyang Technological University (Singapore)



 Course：

Operating Systems

Introduction to Deep Reinforcement LearningIntelligent OptimizationDigital Rural Development & AI


 Projects：

  1.  Knowledge-Augmented High-Performance Trustworthy Genetic Programming, NSFC (2025-2028), Principal Investigator

  2.  Multiform Genetic Programming for High-Dimensional Complex Optimization (2021-2024), PI

  3.  Crowd Behavior Modeling and Simulation via Cooperative Genetic Programming, NSFC (2017-2019), PI

  4.  Knowledge-Driven High-Performance Evolutionary Transfer Optimization, National Foreign Experts Program (2021-2023), PI

  5.  Cross-Modal Intelligent Perception: Theory, Key Technologies and Industrial Applications, Pearl River Innovation Team Project (Total funding: ¥20M, 2018-2023), Subproject Leader

  6.  Data-Driven Computational Intelligence Methods, Guangdong NSF Research Team Program (¥3M, 2018-2023), Core Member & Subproject Leader

  7.  Data-Driven Crowd Behavior Modeling Technology and Applications, China-Singapore International Joint Research Institute Industrialization Project (2019-2021), Co-Principal Investigator

  8.  Knowledge-Enhanced High-Performance Graph Neural Network Optimization, Guangdong NSF (2023-2025), PI

  9.  Intelligent Environmental Control Panel Based on Deep Learning and Computer Vision, Industry Collaboration Project (2024-2025), PI

  10.  Multimodal Large Language Models and 3D Perception AI Chip Systems for Service Robots, Hengqin-Guangdong-Macao Deep Cooperation Zone Key Technology Project (¥10M, 2025-2027), Project Leader


 Publications：

[1] J. Dong, and J. Zhong*, “Recent Advances in Symbolic Regression”, ACM Computing Surveys, 2025, Accepted.  

[2] M. -Y. Zheng, Y. Wang, J. Zhong and J. Zhang, "Discovering Infinite Recursive Conjectures Through Genetic Programming," in IEEE Transactions on Evolutionary Computation, doi: 10.1109/TEVC.2025.3611312.  

[3] X. Han, J. Zhong*, Z. Ma, X. Mu, and N. Gligorovski, “Transformer-assisted Genetic Programming for Symbolic Regression,” IEEE Computational Intelligence Magazine, vol. 20,no. 2, pp. 58-79, May 2025  

[4] J. Zhong, J. Dong, W.-L. Liu, L. Feng and J. Zhang, “Multiform Genetic Programming Framework for Symbolic Regression Problems,” IEEE Transactions on Evolutionary Computation, vol. 29, no. 2, pp. 429-443, April 2025.

[5] X. Han,  X. Mu, J. Zhong*,” HGFF: A Deep Reinforcement Learning Framework for Lifetime Maximization in Wireless Sensor Networks,” IEEE Transactions on Artificial Intelligence, vol. 6, no. 4, pp. 859-873, April 2025.  

[6] J. Dong, J. Zhong*, W. Liu, and J. Zhang, “Evolving Equation Learner For Symbolic Regression”, IEEE Transactions on Evolutionary Computation, 2024, Accepted

[7] 麦伟杰，刘伟莉，钟竞辉*，基于机器学习的演化多任务优化框架，计算机学报，2023，已接收

[8] 钟竞辉，林育钿，李稳强，蔡文桐，基于数字孪生的机场人群智慧管控技术，系统仿真学报，2023-02-17  

[9] Z. Huang, Y. Mei*, and J. Zhong*, “Semantic Linear Genetic Programming for Symbolic Regression,” IEEE Transactions on Cybernetics, vol. 54, no. 2, pp. 1321-1334, Feb. 2024.

[10] J.Zhong, D. Li, W. Cai, W.-N. Chen, Y. Shi, “Automatic Crowd Navigation Path Planning in Public Scenes Through Multiobjective Differential Evolution,” IEEE Transactions on Computational Social Systems, vol. 11, no. 1, pp. 905-918, Feb. 2024


Awards：

• Outstanding Industry-Academia Collaboration Case, KylinSoft (2024)

• IEEE TETCI Outstanding Paper Award, IEEE CIS (2023)

• Natural Science Award (First Class), Ministry of Education (2010)