Xiaolei Fang

Assistant Professor

Phone: 919.515.0312
Email: xfang8@ncsu.edu
Office: 4177 Fitts-Woolard Hall
Website: https://xiaoleifang.wordpress.ncsu.edu/

Personal Website

Research Interests

Fang’s 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, 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.

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

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

Knocking BIG DATA Down to Size

Publications

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, 7. https://doi.org/10.1016/j.ymssp.2022.109014
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
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
Multistream sensor fusion-based prognostics model for systems under multiple operational conditions: Li, X., & Fang, X. (2021), Proceedings of the ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021, 2. https://doi.org/10.1115/MSEC2021-62348
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, Vol. 53, pp. 597–613. 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

View all publications via NC State Libraries