• MEng: 1) Electronic Information  

• MS：1)Intelligent Science and Technology  

• Ph.D: 1) Electronic Information; 2) Intelligent Science and Technology;

Jiang Huaiguang, Ph.D., is currently a tenured professor and doctoral supervisor at the Future Technology College of South China University of Technology. He is also an overseas high-level introduced talent and a member of the Standing Committee of the Guangzhou Youth Federation. By applying technologies such as machine learning, deep learning and large models, multi-scale systems including complex relationship structures, graphs and networks of large-scale interconnected systems are modeled, covering multi-level interaction relationships from protein interactions within cells to human social interactions in society. Specific application fields include DNA design, metabolic network analysis, energy network prediction, comprehensive energy dispatching, and emergency management of smart city clusters, aiming to support the realization of the national strategy of "carbon peak and carbon neutrality". The laboratory is equipped with an advanced Hardware-in-the-loop simulation (HILS) platform, high-performance computing clusters and low-power acceleration devices, which are used to support real-time simulation, signal processing and artificial intelligence model training of complex power systems. Combining the research of digital twin technology and edge computing, we are committed to the cutting-edge exploration and engineering application breakthroughs in smart energy, smart cities and bioinformatics. As the IEEE Senior Member in the Transactions/CVPR IEEE/Applied Energy, such as the top academic conferences or journals published more than 60, special English 2 monographs published, hosted and participated in national projects for many times, I was invited to serve as a reviewer for over twenty top journals and conferences in the industry.

• Low-carbon smart energy and artificial intelligence applications

• S. Li , H. Li, X. Li, Y. Xu, Z. Lin, and H. Jiang*. Causal Intervention is What Large Language Models Need for Spatio-temporal Forecasting, IEEE Transactions on Cybernetics, pp. 1–13, 2025. (JCR Five year impact factor 10.3)

• Y. Zhang, G. Chen, C. Liang, B. Yang, X. Lei, T. Chen, H. Jiang*, and W. Xiong*. Multicrispr-ega: Optimizing guide rna array design for multiplexed crispr using the elitist genetic algorithm. ACS Synthetic Biology, 14(3):919–930, 2025. (JCR Zone 1, CAS Zone 1, top journal of synthetic biology, five-year impact factor 4.2)

• Y. Zhang, Y. Ren, Z. Liu, H. Li, H. Jiang*, Y. Xue, J. Ou, R. Hu, J. Zhang, and D. W. Gao. Federated deep reinforcement learning for varying-scale multi-energy microgrids energy management considering comprehensive security. Applied Energy, 380:125072, 2025. (JCR Five year impact factor 10.4)

• Y. Zhang, R. Lin, Z. Mei, M. Lyu, H. Jiang*, Y. Xue, J. Zhang, and D. W. Gao. Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties. Applied Energy, 376:124155, 2024. (JCR Five year impact factor 10.4)

• S. Li, W. Li, L. Chen, H. Jiang*, J. Zhang, and D. Wenzhong Gao. Real-time robust state estimation for large-scale low-observability power-transportation system based on meta physics-informed graph timesnet. IEEE Transactions on Smart Grid, 15(6):5500–5513, 2024 (JCR Five year impact factor 9.6)

• Y. Zhang, Z. Mei, X. Wu, H. Jiang*, J. Zhang, and W. Gao. Two-step diffusion policy deep reinforcement learning method for low-carbon multi-energy microgrid energy management. IEEE Transactions on Smart Grid, 15(5):4576–4588, 2024 (JCR Five year impact factor 9.6)

• H. Jiang, Y. Zhang, Y. Chen, C. Zhao, and J. Tan. Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing–a hierarchical data-driven approach. Applied energy, 229:756–766, 2018. (JCR Five year impact factor 10.4)

• H. Jiang, Y. Zhang, E. Muljadi, J. J. Zhang, and D. W. Gao. A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization. IEEE Transactions on Smart Grid, 9(4):3341–3350, 2016. (JCR Five year impact factor 9.6)

• H. Jiang, X. Dai, D. W. Gao, J. J. Zhang, Y. Zhang, and E. Muljadi. Spatial-temporal synchrophasor data characterization and analytics in smart grid fault detection, identification, and impact causal analysis. IEEE Transactions on Smart Grid, 7(5):2525–2536, 2016. (JCR Five year impact factor 9.6)

• H. Jiang, Y. Zhang, J. J. Zhang, D. W. Gao, and E. Muljadi. Synchrophasor-based auxiliary controller to enhance the voltage stability of a distribution system with high renewable energy penetration. IEEE Transactions on Smart Grid, 6(4):2107–2115, 2015. (JCR Five year impact factor 9.6)