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.
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.