Georgia Power Professor
Education
- Ph.D. Industrial Engineering (2003), Purdue University
- M.S. Industrial Engineering (1998), Purdue University
- B.S. Production Engineering (1995), University of Alexandria, Egypt
Expertise
- Equipment Predictive Analytics
- Machine Learning
- Reliability and Maintainability Engineering
- Power Generation Applications
- Manufacturing Applications
Research
Dr. Gebraeel's research lies at the intersection of industrial data science, Machine Learning, and optimization. His work emphasizes decentralized (federated) settings in manufacturing, power generation, and aerospace applications where causal-informed analytics enhance decision quality under data heterogeneity and data-sharing constraints. This research program spans two complementary thrusts. The first focuses on developing novel Statistical and Machine Learning methods for real-time equipment diagnostics and prognostics. The second thrust focuses on designing optimization models that translate diagnostic/prognostic insights into optimal decisions to improve reliability and support maintenance, repair, and operational (MRO) decisions for industrial systems.
Teaching
Dr. Gebraeel enjoys teaching courses in reliability and survival analysis, engineering statistics, and industrial predictive analytics.
Awards and Honors
- Fellow Award of the Institute of Industrial and Systems Engineers (IISE)
- Student Recognition of Excellence in Teaching: Class of 1934 Award.
- Georgia Power Early Career Professorship
- Chandler Family Chair
- National Science Foundation CAREER Award
Representative Publications
- Rozas H., Xie W., Gebraeel N., and Robinson S. “Data-driven Joint Optimization of Maintenance and Spare Parts Provisioning: A Distributionally Robust Approach,” European Journal of Operational Research, vol. 328, no. 1, p. 122-136, 2026.
- Mohanty A., Dekarsek J., Joshi, S., and Gebraeel N. “Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities,” IEEE Transactions on Systems Man and Cybernetics: Systems, vol. 56, no. 1, p. 443-457, 2026.
- Ibrahim, M., Gebraeel, N., and Xie, W. “A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components,” International Conference on Representation Learning (ICLR), 2026 https://www.arxiv.org/pdf/2601.21160
- Mohanty, A., Mohamed, N., Ramanan, P., and Gebraeel, N. (2025). “Federated Granger Causality Learning for Interdependent Clients with State Space Representation,” International Conference on Representation Learning (ICLR), 2025 https://arxiv.org/abs/2501.13890
- Karakaya, Ş., Yildirim, M., Gebraeel, N. and Xia, T., “A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems”, International Journal of Production Research, vol. 62, no. 24, p. 8701-8718, 2024.
- Rozas H., Basciftci B., and Gebraeel N., “Data-driven Joint Optimization of Maintenance and Spare Parts Provisioning for Deep Space Habitats: A Stochastic Programming Approach”, Acta astronautica, vol. 214, p. 167-181, 2024.
- Li D., Gebraeel N., Paynabar K., and Meliopoulos A. P. S. “An Online Approach to Cyberattack Detection and Localization in Smart Grid,” IEEE Transactions on Power Systems, vol. 38, no. 1, p. 267-277, 2022
- Ramanan P., Yildirim M., and Gebraeel N., “Differentially Private Decentralized Generator Maintenance and Operations for Power Networks”, IEEE Transactions on Control of Network Systems, vol. 10, no. 2, p. 972-982, 2022.
- Ramanan P., Li D., and Gebraeel N. “A Decentralized Blockchain-based Cyber Threat Detection for Power Systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 8, p. 4727-4739, 2021.
- Ramanan P, Yildirim M, Gebraeel N., Chow E. “Large-Scale Maintenance and Unit Commitment: A Decentralized Subgradient Approach,” IEEE Transactions on Power Systems, vol. 37, no. 1, p. 237-248, 2021.