Professor Nagi Gebraeel is the Georgia Power Early Career Professor and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his MS and PhD from Purdue University in 1998 and 2003, respectively. Dr. Gebraeel’s research interests lie at the intersection of Industrial Predictive Analytics and Machine Learning. His work focuses on developing advanced statistical learning models and novel Machine Learning algorithms for real-time equipment diagnostics and prognostics as well as optimization models for operational and logistical decision-making in IoT industrial applications. Dr. Gebraeel also explores cybersecurity algorithms to safeguard IoT-enabled critical assets against Industrial Control System (ICS) cyberattacks. His application domains span manufacturing, power generation, and service industries, with interests extending to Deep Space Applications through his work with NASA’s HOME Space Technology Research Institute.
Dr. Gebraeel leads the Predictive Analytics and Intelligent Systems (PAIS) research group at Georgia Tech. He also directs activities and reliability testing at the Analytics and Prognostics Systems laboratory at Georgia Tech’s Manufacturing Institute. Dr. Gebraeel is a Fellow of the Institute of Industrial and Systems Engineers. He served as an associate director at Georgia Tech’s Strategic Energy Institute, where he championed research initiatives at the intersection of Data Science and Energy. Dr. Gebraeel has held prominent leadership roles, including as former president of the IISE Quality and Reliability Engineering Division. He is a longstanding member of INFORMS and IISE (since 2005).
Recent News
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.
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.
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.
Congratulations to Dan Li (a 2021 Ph.D. graduate in ISYE and a PAIS alumnus) on her appointment as an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin, Madison.
The PIAS laboratory releases Gustavo, a software tool for orchestrating ML/AI models and analytic algorithms across multiple Edge devices.
