威尼斯娱人城官网

2015.09-2019.05	City University of Hong Kong	School of Data Sacience	Ph.D
Supervisors:
2006.09-2010.06	Shantou University	Department of Mathematics	Bachelor

Uncertainty Quantification
Computer Experiments and Simulation Optimization
Quality and Reliability Engineering
Prognostics and Health Management
Interpretable Machine Learning and Causal Inference

2022/01-2024/12 Statistical Monitoring and Intelligent Diagnosis of Complex Manufacturing Processes Based on Multi-source Heterogeneous Data, NSFC Youth Program, 300, 000 RMB, PI.

2021/10-2023/9, Statistical Process Monitoring and Intelligent Diagnosis of Machinery under Varying Working Conditions through Multiple Heterogeneous Sensors, Shanghai Pujiang Talent Program, 300, 000 RMB, PI.

2022-01-2025-12, Process Monitoring and Maintenance Decision-making of Complex Manufacturing Systems through Joint Modeling of Product Quality and Machine Condition Data, NSFC General Program, 480, 000 RMB, Co-PI.

2016-2020 Safety, Reliability, and Disruption Management of High Speed Rail and Metro Systems Theme-based Research Scheme 40,000,000 HKD, Participant

2016-2019 Statistical Monitoring of Multivariate Quality Profiles Using Correlated Gaussian Processes General Research Fund 676,000 HKD, Participant

2014-2016 Experimental Design schemes for Computer Experiments with Complex Characteristics, National Science Foundation of China, 220,000 RMB, Participant

Yongxiang Li received his Ph.D. degree in data science from City University of Hong Kong in 2019. Currently, he is an Associate Professor in the Department of Industrial Engineering and Management at Shanghai Jiao Tong University. His research focuses on both the theoretical and applied aspects of data science integrated with domain knowledge for quality and reliability engineering using methodologies from statistics, machine learning, and signal processing. He is an associate editor of INFORMS Journal on Data Science.


12) Yongxiang LI, Qiang ZHOU, Wei JIANG, Kwok-Leung TSUI. (2023). Optimal Composite Likelihood Estimation and Prediction for Distributed Gaussian Process Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence. Accepted.

11) Yongxiang LI, Yunji ZHANG, Qian XIAO, Jianguo WU*. (2023). Quasi-Periodic Gaussian Process Modeling of Pseudo-Periodic Signals. IEEE Transactions on Signal Processing. Accepted. DOI: 10.1109/TSP.2023.3316589

10) Yushuai CHE, Yan MA, Yongxiang LI, and Linhan OUYANG*. (2023). A Novel Active-learning Kriging Reliability Analysis Method based on Parallelized Sampling Considering Budget Allocation. IEEE Transactions on Reliability. Accepted.

9) Ruixian LI, Jianguo WU, Yongxiang LI*, Yao CHENG. (2023). PeriodNet: Noise-Robust Fault Diagnosis Method under Varying Speed Conditions. IEEE Transactions on Neural Networks and Learning Systems. Accepted. DOI: 10.1109/TNNLS.2023.3274290

8) Yongxiang LI, Yuting Pu, Qian XIAO*, Changming CHENG. (2023). A Scalable Gaussian Process for Large-Scale Periodic Data. Technometrics. 65(3), 363–374.

7) Wei FAN, Zhenqiang CHEN, Yongxiang LI*, Feng ZHU, Min XIE. (2023). A Reinforced Noise Resistant Correction Method for Bearing Condition Monitoring. IEEE Transactions on Automation Science and Engineering. 20(2), 995-1006.

6) Yongxiang LI, Yuting Pu, Wei FAN*, Jianguo WU. (2023). Constraint Linear Model for Period Estimation and Sparse Feature Extraction Based on Iterative Likelihood Ratio Test. IEEE Transactions on Industrial Electronics. 70(4), 4196-4205.

5) Ruiyu XU, Jianguo WU*, Xiaowei YUE, Yongxiang LI. (2023). Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learning. Technometrics. 65(1), 19-32.

4) Mithun GHOSH, Yongxiang LI, Qiang ZHOU*, Li ZENG, Zijun ZHANG. (2021). Modeling Multivariate Profiles Using Gaussian Process-Controlled B-Splines. IISE Transactions. 53(7), 787-798.

3) Wei FAN, Yongxiang LI*, Kwok-Leung TSUI, Qiang ZHOU. (2018). A Noise Resistant Correlation Method for Period Detection of Noisy Signals. IEEE Transactions on Signal Processing. 66(10) 2700-2710.

2) Yongxiang LI, Qiang ZHOU*, Xiaohu HUANG, Li ZENG. (2018). Pairwise Estimation of Multivariate Gaussian Process Models with Replicated Observations: Application to Multivariate Profile Monitoring. Technometrics. 60(1), 70-78.

1) Yongxiang LI, Qiang ZHOU*. (2016). Pairwise Meta-Modeling of Multivariate Output Computer Models Using Nonseparable Covariance Function. Technometrics. 58(4), 483-494.

Engineering Statistics (key course at SJTU) Spring 2020 / 2021 / 2022 / 2023
Comprehensive Experiment for Industrial Engineering Fall 2022 / 2023

，Associate Editor
INFORMS, IISE, and IEEE members

 Shanghai Pujiang Talent Program (2021)
 First Prize of Teaching Achievement Award at Shanghai Jiao Tong University (2023). Ranking the 5th
 Online Structural Change-point Detection of High dimensional Streaming Data via Dynamic Sparse Subspace Learning. The Finalist of INFORMS 2020 QSR Best Student Paper Award. (Ruiyu XU; Jianguo WU, Xiaowei YUE, Yongxiang LI)
 Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior. The Finalist of INFORMS 2023 Data Mining Best Paper Award. (Xinming WANG, Yongxiang LI, Xiaowei YUE, Jianguo WU)
 Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering. INFORMS 2023 Data Mining Best Student Paper Winner. (Ruiyu XU, Chao WANG, Yongxiang LI, Jianguo WU)