Bio

Xiaolei Fang is an associate professor at NC State University. He has been a faculty member at NC State since 2018, first serving as an Assistant Professor before being promoted to Associate Professor in August 2024. In this role, he is involved in teaching, research, and service within the university’s engineering community.

Before joining NC State, Xiaolei worked as a Research Scientist at DecisionIQ, an AI consulting firm in the Greater Atlanta Area. He earned his Ph.D. in Industrial Engineering from the Georgia Institute of Technology in 2018. During the same period, he also completed a Master of Science in Statistics at the Georgia Institute of Technology. His academic background reflects a strong foundation in industrial engineering, data analysis, and quantitative methods.

Xiaolei’s expertise encompasses programming and C++, as well as a broad range of technical and analytical skills. He has received several honors, including the Alice and John Jarvis Ph.D. Student Research Award from Georgia Tech. His work has also been featured in ISE Magazine, highlighting the impact of his research and contributions to the field of industrial and systems engineering.

Area(s) of Expertise

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.
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

Publications

• Deep Complex Wavelet Denoising Network for Interpretable Fault Diagnosis of Industrial Robots With Noise Interference and Imbalanced Data , IEEE Transactions on Instrumentation and Measurement (2025)
• Deep Learning-Based Residual Useful Lifetime Prediction for Assets With Uncertain Failure Modes , Journal of Computing and Information Science in Engineering (2025)
• Enhancing Data Privacy in Human Factors Studies with Federated Learning , Human Factors The Journal of the Human Factors and Ergonomics Society (2025)
• A distributionally robust chance-constrained kernel-free quadratic surface support vector machine , European Journal of Operational Research (2024)
• A federated data fusion-based prognostic model for applications with multi-stream incomplete signals , IISE Transactions (2024)
• Distributionally robust chance-constrained kernel-based support vector machine , Computers & Operations Research (2024)
• IISE PG&E Energy Analytics Challenge 2024: Forecasting day-ahead electricity prices , IISE Transactions (2024)
• Image-based remaining useful life prediction through adaptation from simulation to experimental domain , Reliability Engineering & System Safety (2024)
• Learning Undergraduate Data Science Through a Mobile Device and Full Body Movements , TechTrends (2024)
• Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol , Journal of Manufacturing Systems (2024)

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Grants

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