Xiaolei Fang

Assistant Professor



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.

  • Data Science
  • Machine Learning
  • Artificial Intelligence


  • Condition Monitoring
  • Anomalies Detection
  • Fault Root-Cause Diagnostics
  • Degradation Modeling and Failure Time Prognostics
  • System Performance Assessment, Optimization, Decision-making, and Control



Ph.D.Industrial EngineeringGeorgia Institute of Technology2014-2018
MSStatisticsGeorgia Institute of Technology2014-2016
BSMechanical EngineeringUniversity of Science and Technology Beijing2004-2008

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



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 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
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
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
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

View all publications via NC State Libraries

Xiaolei Fang