Research Interests

Low-Carbon Smart Energy: Resilient energy systems and intelligent energy storage; forecasting and control of integrated energy systems; energy-efficient computing and high-efficiency large language models; digital twins and resilient cities.

Intelligent Design of Complex Biological Systems: Protein structure and function prediction; large-model-enabled synthetic biology system design; AI-driven optimization of metabolic networks and digital twins of bioprocesses.

Autonomy & Automation: Large-scale distributed optimization and high-dimensional stochastic optimization; intelligent human-machine hybrid systems and multi-system coordination; safety and health automation for complex systems; automation and explainable AI.

Biography

I am a tenured professor and doctoral advisor at the School of Future Technology, South China University of Technology (SCUT). I am a recipient of High-Level Overseas Talents and serve on the Standing Committee of the Guangzhou Youth Federation.

My work applies machine learning, deep learning, and large language models to model multi-scale systems characterized by complex relational structures, graphs, and networks—spanning from intracellular protein-protein interactions to human social interactions. Application domains include DNA design, metabolic network analysis, energy network forecasting and integrated energy dispatch, and emergency management for smart city clusters—aimed at advancing China’s “carbon peak and carbon neutrality” strategy.

My lab is equipped with advanced hardware-in-the-loop simulation (HILS) platforms, high-performance computing clusters, and low-power accelerators to support real-time simulation of complex power systems, signal processing, and AI model training. By integrating digital twins technologies with edge computing, my group pursues cutting-edge research and engineering breakthroughs in smart energy, smart cities, and bioinformatics.

As an IEEE Senior Member, I have published 60+ papers in top conferences and journals such as IEEE Transactions, CVPR, and Applied Energy, and I have been invited to author two English-language monographs. I have led and participated in multiple national-level projects and serve as a reviewer for more than twenty leading journals and top conferences.

I emphasize a strong foundation in theory coupled with hands-on practice. Through innovative research projects, I encourage students to develop creativity and grow into domain experts capable of independent leadership. My students have received numerous national and provincial/ministerial awards, including the National Scholarship, Intel Scholarship, and SCUT's “Top Ten Outstanding Students.” Graduates pursue diverse paths, including Chinese central State-owned enterprises (e.g., State Grid, China Southern Power Grid), internet companies (e.g., Tencent), and further study at top universities (e.g., guaranteed admission to Peking University and Zhejiang University).

Each year I recruit 1-2 PhD students and 2-4 master's students; postdoctoral researchers are welcomed on a rolling basis.

Contact: hihuagong2021@scut.edu.cn

Education

2015, Ph.D., University of Denver

2010, M.S., University of Electronic Science and Technology of China (UESTC)

2007, B.S., National University of Defense Technology (NUDT)

Work Experience

2015-2021, National Renewable Energy Laboratory (NREL), USA

2021-present, Tenured Professor, School of Future Technology, South China University of Technology

Courses Taught

Python Language Programming, bilingual (Chinese/English), 32 contact hours

Selected Publications

Monograph

H. Jiang, Y. Zhang, and E. Muljadi, “New Technologies for Power System Operation and Analysis, 1st Edition”, Academic Press (an imprint of Elsevier), 2020 (First author)

Most Recent Ten Publications

1. 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 Q1; CAS Q1; five-year impact factor 10.3)

2. 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 Q1; CAS Q1; five-year impact factor 4.2)

3. 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 Q1; CAS Q1; five-year impact factor 10.4)

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 Q1; CAS Q1; five-year impact factor 10.4)

5. 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 Q1; CAS Q1; five-year impact factor 9.6)

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 Q1; CAS Q1; five-year impact factor 9.6)

7. 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 Q1; CAS Q1; five-year impact factor 10.4)

8. 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 Q1; CAS Q1; five-year impact factor 9.6)

9. 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 Q1; CAS Q1; five-year impact factor 9.6)

10. 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 Q1; CAS Q1; five-year impact factor 9.6)