Hui Wang | Network Resilience | Best Researcher Award

Published on by Network Science

Dr. Hui Wang | Network Resilience | Best Researcher Award

Southwest Jiaotong University | China

Dr. Hui Wang is an Associate Researcher at Southwest Jiaotong University, where he also earned his Doctoral degree after completing his Bachelor’s studies at the University of South China. His research focuses on intelligent control using deep reinforcement learning, data-efficient embodied AI that learns across physical and virtual environments, physics-informed modeling and simulation, and computer-vision-based perception and defect detection. He is proficient in Python (PyTorch/TensorFlow), MATLAB, and C, with extensive experience in data communication and hardware-in-the-loop systems. Dr. Wang has been recognized with major honors, including the Outstanding Doctoral Dissertation Award from Southwest Jiaotong University (2024) and the National Scholarship for Doctoral Degree (2023). He has led and contributed to several national-level research projects, notably developing a physics-informed simulation engine and resilient pantograph control algorithms for the National Natural Science Foundation of China, designing intelligent high-speed railway pantograph systems for the China Postdoctoral Science Foundation, and advancing deep reinforcement learning applications for high-speed rail as part of the Sichuan Science and Technology Plan. His earlier work includes pioneering machine-learning-based monitoring methods for railway catenary components, developing CNN-based detection models, unsupervised learning frameworks, and segmentation-assisted diagnosis tools.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Multi-modal imitation learning for arc detection in complex railway environments",  J Yan, Y Cheng, F Zhang, N Zhou, H Wang, B Jin, M Wang, W Zhang, IEEE Transactions on Instrumentation and Measurement, 2025.

"Research on multimodal techniques for arc detection in railway systems with limited data",  J Yan, Y Cheng, F Zhang, M Li, N Zhou, B Jin, H Wang, H Yang, W Zhang, Structural Health Monitoring, 14759217251336797, 2025.

"CSRM-MIM: A Self-Supervised Pre-training Method for Detecting Catenary Support Components in Electrified Railways", H Yang, Z Liu, N Ma, X Wang, W Liu, H Wang, D Zhan, Z Hu, IEEE Transactions on Transportation Electrification, 2025.

"Assessment of current collection performance of rail pantograph-catenary considering long suspension bridges", X Wang, Y Song, B Lu, H Wang, Z Liu, IEEE Transactions on Instrumentation and Measurement, 2025.

"FENet: A Physics‐Informed Dynamics Prediction Model of Pantograph‐Catenary Systems in Electric Railway", W Chu, H Wang, Y Song, Z Liu, 2025.

Qinglai Wei | Self-Learning Systems | Best Researcher Award

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Prof. Dr. Qinglai Wei | Self-Learning Systems | Best Researcher Award 

Associate Director, at Institute of Automation, Chinese Academy of Sciences, China.

Professor Qinglai Wei is a distinguished researcher and educator specializing in control systems, computational intelligence, and learning-based optimization. Serving as the Associate Director at The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, he has made significant contributions to adaptive dynamic programming, nonlinear control, and reinforcement learning. With an illustrious academic journey from Northeastern University and rich professional experience, Prof. Wei has authored numerous influential papers, books, and book chapters. His awards include multiple IEEE honors and recognition as a Clarivate Highly Cited Researcher. He is a prominent figure in advancing intelligent control systems and their applications in complex scenarios.

Professional Profile

Scopus

Google Scholar

Education 🎓

  • Ph.D. in Control Theory and Control Engineering (2009): Northeastern University, China. Advised by Prof. Huaguang Zhang, his research focused on intelligent control systems.
  • M.S. in Control Theory and Control Engineering (2005): Northeastern University, China, under Prof. Xianwen Gao’s mentorship.
  • B.S. in Automation (2002): Northeastern University, China, advised by Baodong Xu.
    These academic milestones laid the foundation for his expertise in adaptive dynamic programming and intelligent systems.

Professional Experience 💼

  • Associate Director (2018–Present): The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences.
  • Professor (2016–Present): The State Key Laboratory and the School of Artificial Intelligence, University of Chinese Academy of Sciences.
  • Visiting Scholar roles at University of Rhode Island (2018) and University of Texas at Arlington (2014) reflect his international collaboration and academic outreach.
    Earlier roles include Associate and Assistant Professor positions at The State Key Laboratory, showcasing steady growth in his academic career.

Research Interests 🔬

Prof. Wei’s research spans:

  • Computational Intelligence & Intelligent Control
  • Learning Control & Reinforcement Learning
  • Optimal & Nonlinear Control
  • Adaptive Dynamic Programming
    Applications include process control, smart grids, and multi-agent systems. His innovative methods continue to drive advancements in control theory and intelligent systems.

Awards 🏆

Prof. Wei’s excellence is marked by accolades like:

  • Best Paper Awards (2023 & 2022): International CSIS-IAC and China Automation Congress.
  • IEEE Outstanding Paper Awards (2018): Recognition for impactful contributions to the IEEE journals.
  • Highly Cited Researcher (2018 & 2019): By Clarivate Analytics for his influential publications.
    Other honors include National Natural Science Foundation Awards and Young Researcher Awards, emphasizing his leadership in the field.

Top Noted Publications 📚

  • “Learning and Controlling Multiscale Dynamics in Spiking Neural Networks” (2024, IEEE Transactions on Cybernetics): This study employs Recursive Least Square (RLS) modifications to manage multiscale dynamics in spiking neural networks. It advances neural control methods for adaptive tasks in dynamic environments【8】.
  • “Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems” (2024, IEEE/CAA Journal of Automatica Sinica): This paper focuses on event-triggered mechanisms to ensure robust consensus in multiagent systems under parallel optimal control.
  • “Primal-Dual Adaptive Dynamic Programming for Nonlinear Systems” (2024, Automatica): A framework using primal-dual adaptive dynamic programming tackles the stabilization and optimization of nonlinear systems.
  • “Class-Incremental Learning with Balanced Embedding Discrimination” (2024, Neural Networks): This work enhances class-incremental learning by introducing techniques to balance embeddings and improve discrimination among new and existing classes.

Conclusion

Qinglai Wei is exceptionally suited for the Research for Best Researcher Award. His prolific contributions to control theory, computational intelligence, and reinforcement learning, combined with his global recognition and leadership, exemplify his stature as a world-class researcher. With a proven track record of innovative research, impactful publications, and numerous accolades, he stands out as a strong candidate for this prestigious honor. Continued expansion into interdisciplinary collaborations and mentorship initiatives will further solidify his legacy as a pioneering researcher.