LI Yongxiang

Li Yongxiang is an associate professor in the Department of Industrial Engineering and Management at Shanghai Jiao Tong University. His research focuses on AI and uncertainty quantification for quality and reliability, including surrogate modeling, Bayesian optimization, control chart methods, fault diagnosis, and remaining useful life prediction. He has led and participated in several research projects funded by the National Natural Science Foundation of China and the Shanghai Pujiang Talent Program. Li has published numerous papers in top-tier journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Signal Processing, and Technometrics. He is also a member of IEEE, INFORMS, and IISE, and has been recognized with awards including the Shanghai Pujiang Talent Program and the First Prize of Teaching Achievement Award at Shanghai Jiao Tong University.

Yongxiang Li is a researcher at Shanghai Jiao Tong University, specializing in Statistics, Machine Learning, and Industrial Engineering. His work focuses on fault diagnosis, signal processing, and condition monitoring in industrial systems. Li's research includes developing noise-robust methods for fault detection under varying speed conditions, such as PeriodNet and generalized autocorrelation techniques. He also explores multivariate Gaussian process models and sparse feature extraction for periodic signals. His contributions span deep learning architectures for failure mode diagnostics and scalable Gaussian processes for large-scale periodic data. Li's research addresses challenges in real-time monitoring and reliability analysis of mechanical systems.