I received the Ph.D. degree in Electrical Engineering from the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China. He is currently a postdoctoral researcher at the same institution. His research focuses on intelligent perception, condition monitoring, and data-driven decision-making for railway transportation infrastructure, with emphasis on electrified railway systems. His work includes anomaly detection of catenary supporting components, high-precision geometric parameter measurement based on 3D point clouds, and UAV-enabled intelligent inspection for railway infrastructure. He also studies rail–wheel interaction defect detection, including rail surface defects and wheel tread damage, as well as bearing fault diagnosis and remaining useful life prediction for railway rolling stock. In addition, he investigates pantograph active control strategies using reinforcement learning to improve current collection stability and reduce wear in high-speed railway operations. His methodology integrates multimodal data fusion and multi-sensor perception, including UAV imagery, LiDAR point clouds, vibration signals, and operational data. He extensively applies computer vision, deep learning, and multimodal representation learning to extract robust features in complex outdoor railway environments. Furthermore, he explores reinforcement learning and data-driven decision-making frameworks to enhance intelligent railway inspection and maintenance systems.