Xiaolei Fang

Associate Professor

 

Personal Website

 

Research Interests

Fang’s research interests lie in 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

DegreeProgramSchoolYear
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

 

Publications

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 federated data fusion-based prognostic model for applications with multi-stream incomplete signals
Arabi, M., & Fang, X. (2024, June 10), 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), COMPUTERS & OPERATIONS RESEARCH, 170. https://doi.org/10.1016/j.cor.2024.106755
Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol
Koprov, P., Fang, X., & Starly, B. (2024), JOURNAL OF MANUFACTURING SYSTEMS, 76, 59–74. https://doi.org/10.1016/j.jmsy.2024.07.003
Tensor-based statistical learning methods for diagnosing product quality defects in multistage manufacturing processes
Jeong, C., Byon, E., He, F., & Fang, X. (2024, August 9), IISE TRANSACTIONS, Vol. 8. https://doi.org/10.1080/24725854.2024.2385670
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
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
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
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

View all publications via NC State Libraries

Xiaolei Fang