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
- DISTRIBUTIONALLY ROBUST OPTIMIZATION: A REVIEW ON THEORY AND APPLICATIONS
- Lin, F., Fang, X., & Gao, Z. (2022). [Review of , ]. NUMERICAL ALGEBRA CONTROL AND OPTIMIZATION. https://doi.org/10.3934/naco.2021057
- 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. https://doi.org/10.1115/1.4049963
- 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. https://doi.org/10.1016/j.jmsy.2021.09.010
- Remaining useful life prediction based on a multi-sensor data fusion model
- Li, N., Gebraeel, N., Lei, Y., Fang, X., Cai, X., & Yan, T. (2021), RELIABILITY ENGINEERING & SYSTEM SAFETY, 208. https://doi.org/10.1016/j.ress.2020.107249
- Two-dimensional variable selection and its applications in the diagnostics of product quality defects
- Jeong, C., & Fang, X. (2021), IISE TRANSACTIONS. https://doi.org/10.1080/24725854.2021.1904524
- Multi-sensor prognostics modeling for applications with highly incomplete signals
- Fang, X., Yan, H., Gebraeel, N., & Paynabar, K. (2020), IISE TRANSACTIONS. https://doi.org/10.1080/24725854.2020.1789779
- Image-Based Prognostics Using Penalized Tensor Regression
- Fang, X., Paynabar, K., & Gebraeel, N. (2019), TECHNOMETRICS, 61(3), 369–384. https://doi.org/10.1080/00401706.2018.1527727
- Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization
- Xia, T., Fang, X., Gebraeel, N., Xi, L., & Pan, E. (2019), JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 141(5). https://doi.org/10.1115/1.4043255
- Prognostic and health management for adaptive manufacturing systems with online sensors and flexible structures
- Dong, Y., Xia, T., Fang, X., Zhang, Z., & Xi, L. (2019), COMPUTERS & INDUSTRIAL ENGINEERING, 133, 57–68. https://doi.org/10.1016/j.cie.2019.04.051
