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.

Ali Nikoutadbir | Intelligent Transportation Systems | Young Researcher Award

Published on by Network Science

Mr. Ali Nikoutadbir | Intelligent Transportation Systems | Young Researcher Award

Tarbiat Modares University Faculty of Electrical and Computer Engineering | Iran

Ali Nikoutadbir is a motivated and results-driven MSc graduate in Electrical Engineering with over six years of research experience in securing cyber-physical systems (CPS), particularly focusing on multi-agent systems and intelligent transportation networks. His expertise lies in developing innovative graph-theoretic and optimization-based algorithms to tackle challenges in distributed coordination control, event-triggered control, and the resilience of industrial CPS. As a Research Assistant at the Control System Science Lab, Tarbiat Modares University, Tehran, Iran (2020–2023), he designed and implemented a novel secure event-triggered control framework for vehicular platoons to counter dual deception attacks, including false data injection and global manipulation. He also developed static and dynamic event-triggering schemes ensuring secure consensus under stringent attack constraints and introduced a topology-switching strategy based on Schur stability to enhance system resilience. His theoretical advancements were validated through extensive MATLAB/Simulink simulations. Ali’s published work, including “Secure event-triggered control for vehicle platooning against dual deception attacks,” forms the foundation of his contributions to CPS security. His research expertise encompasses securing multi-agent networks against deception attacks using graph-theoretic and optimization-based methodologies, stability analysis, and end-to-end system modeling, design, and validation.

Profiles: Google Scholar

Featured Publications

"Secure event-triggered control for vehicle platooning in the presence of modification attacks"

"Secure event-triggered control for vehicle platooning against dual deception attacks"

"Estimation of the error caused by the vibration of the radar in the SAR radar aperture using the analytical condition empirical method"