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
- Deep Complex Wavelet Denoising Network for Interpretable Fault Diagnosis of Industrial Robots With Noise Interference and Imbalanced Data
- Li, R., Xia, T., Jiang, Y., Wu, J., Fang, X., & Gebraeel, N. (2025, January 1), IEEE Transactions on Instrumentation and Measurement, Vol. 74. https://doi.org/10.1109/TIM.2025.3540131
- Deep Learning-Based Residual Useful Lifetime Prediction for Assets With Uncertain Failure Modes
- Su, Y., & Fang, X. (2025, February 6), Journal of Computing and Information Science in Engineering, Vol. 2. https://doi.org/10.1115/1.4067843
- Enhancing Data Privacy in Human Factors Studies with Federated Learning
- Su, B., Qing, L., Lu, L., Jung, S. H., Fang, X., & Xu, X. (2025, June 6), Human Factors The Journal of the Human Factors and Ergonomics Society, Vol. 6. https://doi.org/10.1177/00187208251348025
- A distributionally robust chance-constrained kernel-free quadratic surface support vector machine
- Lin, F., Fang, S.-C., Fang, X., Gao, Z., & Luo, J. (2024, February 22), European Journal of Operational Research, Vol. 316, pp. 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, May 29), 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, July 3), Computers & Operations Research, Vol. 170. https://doi.org/10.1016/j.cor.2024.106755
- IISE PG&E Energy Analytics Challenge 2024: Forecasting day-ahead electricity prices
- Ezzat, A. A., Mansouri, M., Yildirim, M., & Fang, X. (2024, December 27), IISE Transactions, Vol. 1. https://doi.org/10.1080/24725854.2024.2447049
- Image-based remaining useful life prediction through adaptation from simulation to experimental domain
- Wang, Z., Yang, L., Fang, X., Zhang, H., & Xie, M. (2024, November 26), Reliability Engineering & System Safety, Vol. 255. https://doi.org/10.1016/j.ress.2024.110668
- Learning Undergraduate Data Science Through a Mobile Device and Full Body Movements
- Jung, S. H., 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, July 26), Journal of Manufacturing Systems, Vol. 76, pp. 59–74. https://doi.org/10.1016/j.jmsy.2024.07.003
