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 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
- 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
- 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
- DISTRIBUTIONALLY ROBUST OPTIMIZATION: A REVIEW ON THEORY AND APPLICATIONS
- Lin, F., Fang, X., & Gao, Z. (2022). [Review of , ]. NUMERICAL ALGEBRA CONTROL AND OPTIMIZATION, 12(1), 159–212. https://doi.org/10.3934/naco.2021057
- Spatiotemporal denoising wavelet network for infrared thermography-based machine prognostics integrating ensemble uncertainty
- Jiang, Y., Xia, T., Wang, D., Fang, X., & Xi, L. (2022), MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 173. https://doi.org/10.1016/j.ymssp.2022.109014
- Two-dimensional variable selection and its applications in the diagnostics of product quality defects
- Jeong, C., & Fang, X. (2022), IISE TRANSACTIONS, 54(7), 619–629. https://doi.org/10.1080/24725854.2021.1904524
- Infrared image stream based regressors for contactless machine prognostics
- Dong, Y., Xia, T., Wang, D., Fang, X., & Xi, L. (2021), MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 154. https://doi.org/10.1016/j.ymssp.2020.107592
- Integrated Remanufacturing and Opportunistic Maintenance Decision-Making for Leased Batch Production Lines
- Xia, T., Zhang, K., Sun, B., Fang, X., & Xi, L. (2021), JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 143(8). https://doi.org/10.1115/1.4049963
- Multi-sensor prognostics modeling for applications with highly incomplete signals
- Fang, X., Yan, H., Gebraeel, N., & Paynabar, K. (2021), IISE TRANSACTIONS, 53(5), 597–613. https://doi.org/10.1080/24725854.2020.1789779
- Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process
- Qian, Q., Fang, X., Xu, J., & Li, M. (2021), JOURNAL OF MANUFACTURING SYSTEMS, 61, 375–390. https://doi.org/10.1016/j.jmsy.2021.09.010
