Xiaolei Fang

Assistant Professor

 

Personal Website

Xiaolei Fang’s research interest lies in the field of industrial data analytics for high-dimensional and big data applications in the energy, manufacturing, and service sectors.

Methodologies:

  • Data science
  • Machine learning

Applications:

  • System performance assessment and optimization
  • System anomalies detection
  • Fault root causes diagnostics
  • Remaining useful lifetime prediction
  • Decision-making and control

Education

Ph.D. 2014-2018

Industrial Engineering

Georgia Institute of Technology

MS 2014-2016

Statistics

Georgia Institute of Technology

BS 2004-2008

Mechanical Engineering

University of Science and Technology Beijing

Research Description

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.

Honors and Awards

  • Winner, Sigma Xi Best Ph.D. Thesis Award, Georgia Institute of Technology, 2019
  • Winner, Alice and John Jarvis Ph.D. Student Research Award, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, 2018
  • Feature Article in ISE Magazine, 2017
  • Winner, SAS Data Mining Best Paper Award, INFORMS, 2016
  • Finalist, QSR Best Refereed Paper Award, INFORMS, 2016

Publications

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., & 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. (2019), COMPUTERS & INDUSTRIAL ENGINEERING, 133, 57–68. https://doi.org/10.1016/j.cie.2019.04.051

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