Xiaolei Fang
Assistant Professor
- Phone: 919.515.0312
- Email: xfang8@ncsu.edu
- Office: 4177 Fitts-Woolard Hall
- Website: https://xiaoleifang.wordpress.ncsu.edu/
My research interests lie in the field of industrial data analytics for High-Dimensional and Big Data applications in the energy, manufacturing, and service sectors. Specifically, I focus on addressing analytical, computational, and scalability challenges associated with the development of statistical and optimization methodologies for analyzing massive amounts of complex data structures for real-time asset management and optimization.
Methodologies
- Data Science
- Machine Learning
- Artificial Intelligence
Applications:
- Condition Monitoring
- Anomalies Detection
- Fault Root-Cause Diagnostics
- Degradation Modeling and Failure Time Prognostics
- System Performance Assessment, Optimization, Decision-making, and Control
Personal Website
Research Interests
Xiaolei Fang’s research focuses on the field of industrial data analytics for High-Dimensional and Big Data applications in the energy, manufacturing, and service sectors. Specifically, he focuses on addressing analytical, computational, and scalability challenges associated with the development of statistical and optimization methodologies for analyzing massive amounts of complex data structures for real-time asset management and optimization.
Education
| Degree | Program | School | Year |
|---|---|---|---|
| Ph.D. | Industrial Engineering | Georgia Institute of Technology | 2014-2018 |
| MS | Statistics | Georgia Institute of Technology | 2014-2016 |
| BS | Mechanical Engineering | University of Science and Technology Beijing | 2004-2008 |
Honors and Awards
- 2019 | Winner, Sigma Xi Best Ph.D. Thesis Award, Georgia Institute of Technology
- 2018 | Winner, Alice and John Jarvis Ph.D. Student Research Award, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology
- 2017 | Feature Article in ISE Magazine
- 2016 | Finalist, QSR Best Refereed Paper Award, INFORMS
- 2016 | Winner, SAS Data Mining Best Paper Award, INFORMS
Discover more about Xiaolei Fang
Publications
- A distributionally robust chance-constrained kernel-free quadratic surface support vector machine
- Lin, F., Fang, S.-C., Fang, X., Gao, Z., & Luo, J. (2024), EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 316(1), 46–60. https://doi.org/10.1016/j.ejor.2024.02.022
- A federated data fusion-based prognostic model for applications with multi-stream incomplete signals
- Arabi, M., & Fang, X. (2024, June 10), IISE TRANSACTIONS, Vol. 6. https://doi.org/10.1080/24725854.2024.2360619
- Distributionally robust chance-constrained kernel-based support vector machine
- Lin, F., Fang, S.-C., Fang, X., & Gao, Z. (2024), COMPUTERS & OPERATIONS RESEARCH, 170. https://doi.org/10.1016/j.cor.2024.106755
- Learning Undergraduate Data Science Through a Mobile Device and Full Body Movements
- Jung, S., Wang, H., Su, B., Lu, L., Qing, L., Fang, X., & Xu, X. (2024, November 27), TECHTRENDS, Vol. 11. https://doi.org/10.1007/s11528-024-01026-0
- Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol
- Koprov, P., Fang, X., & Starly, B. (2024), JOURNAL OF MANUFACTURING SYSTEMS, 76, 59–74. https://doi.org/10.1016/j.jmsy.2024.07.003
- Tensor-based statistical learning methods for diagnosing product quality defects in multistage manufacturing processes
- Jeong, C., Byon, E., He, F., & Fang, X. (2024, August 9), IISE TRANSACTIONS, Vol. 8. https://doi.org/10.1080/24725854.2024.2385670
- Sparse Hierarchical Parallel Residual Networks Ensemble for Infrared Image Stream-Based Remaining Useful Life Prediction
- Jiang, Y., Xia, T., Fang, X., Wang, D., Pan, E., & Xi, L. (2023), IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 19(10), 10613–10623. https://doi.org/10.1109/TII.2022.3229493
- Systems and methods for authenticating manufacturing Machines through an unobservable fingerprinting system
- Koprov, P., Gadhwala, S., Walimbe, A., Fang, X., & Starly, B. (2023), Manufacturing Letters, 35, 1009–1018. https://doi.org/10.1016/j.mfglet.2023.08.051
- A convex two-dimensional variable selection method for the root-cause diagnostics of product defects
- Zhou, C., & Fang, X. (2023), RELIABILITY ENGINEERING & SYSTEM SAFETY, 229. https://doi.org/10.1016/j.ress.2022.108827
- Adversarial Regressive Domain Adaptation Approach for Infrared Thermography-Based Unsupervised Remaining Useful Life Prediction
- Jiang, Y., Xia, T., Wang, D., Fang, X., & Xi, L. (2022), IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 18(10), 7219–7229. https://doi.org/10.1109/TII.2022.3154789
