Professor Nagi Gebraeel is the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research integrates industrial analytics and optimization through two complementary research thrusts: (1) developing statistical learning methods for real-time equipment diagnostics and prognostics, and (2) designing optimization models that translate predictive insights into operational and logistical decisions. His work emphasizes decentralized (federated) settings across manufacturing, power, and aerospace, where causal-informed analytics enhance decision quality under data heterogeneity and data-sharing constraints in high-consequence industrial systems.
Dr. Gebraeel also leads the Predictive Analytics and Intelligent Systems (PAIS) research group and directs reliability testing facilities at the Analytics and Prognostics Systems laboratory at Georgia Tech’s Manufacturing Institute. He is a Fellow of the Institute of Industrial and Systems Engineers. Dr. Gebraeel has held prominent leadership roles as former associate director at Georgia Tech’s Strategic Energy Institute, where he championed research initiatives at the intersection of Data Science and Energy, and president of the IISE Quality and Reliability Engineering Division. He is a longstanding member of INFORMS and IISE (since 2005).
Recent News
August 2025: PAIS Laboratory has secured a $500,000 grant from the National Science Foundation to develop cutting-edge AI techniques that strengthen cybersecurity across distributed manufacturing networks. May 2025: Congratulations to Michael Ibrahim for receiving the 2025 Data Analytics & Information Systems Division (DAIS) Best Student Paper at the 2025 Annual IISE Conference for his work entitled, "A Federated Distributionally Robust Support Vector Machine via Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis". May 2025: Congratulations to my students, Michael Ibrahim and Heraldo Rozas, for having their paper titled "FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity" accepted to the Conference on Uncertainty in Artificial Intelligence (UAI) 2025. April 2025: Congratulations to my students, Ayush Mohanty and Nazal Mohamed, for having their paper titled "Federated Granger Causality Learning for Interdependent Clients with State Space Representation" accepted to the International Conference on Learning Representations (ICLR) 2025. August 2024: Congratulations to Heraldo Rozas (a 2024 PhD-ISYE and PAIS alumnus) on his appointment as an Assistant Professor in the Department of Electrical Engineering at the University of Chile.
