Research Scientists
Senior Research Scientist
TEAM MEMBER
1 / L4 R4120
Dr. Fouzi Harrou is a Senior Research Scientist at King Abdullah University of Science and Technology (KAUST), with nearly 20 years of experience in anomaly detection, process monitoring, artificial intelligence, predictive modeling, and data-driven decision support. He received a Masterʼs degree in Information, Systems and Technology from the University of Paris XI in 2006 and a Ph.D. in Systems Optimization and Security from the University of Technology of Troyes (UTT) in 2010. He also obtained the Habilitation to Direct Research (HDR) from Université Bourgogne Europe, qualifying him for independent research leadership and doctoral supervision. His current research focuses on AI-enabled water and desalination systems, including seawater reverse-osmosis monitoring, membrane fouling and biofouling detection, time-series forecasting, predictive maintenance, digital twins, uncertainty quantification, and explainable AI. His broader research interests include renewable energy, environmental monitoring, and industrial systems. Dr. Harrou has authored more than 250 peer-reviewed publications and two books. He is an IEEE Senior Member and serves as an Associate Editor for Frontiers in Sensors, Discover Artificial Intelligence, and Discover Informatics. He has also been recognized among Stanfordʼs Worldʼs Top 2% Scientists for several consecutive years.
His research aims to develop reliable, explainable, and deployable AI solutions for sustainable water treatment, desalination, and intelligent infrastructure.
My research focuses on developing advanced artificial intelligence, statistical learning, and data-driven methods for monitoring, forecasting, diagnosis, and decision support in complex engineering systems. A major current emphasis is on water desalination, including intelligent monitoring, membrane fouling assessment, predictive maintenance, and operational optimization. I am also interested in explainable AI, uncertainty quantification, physics-informed modeling, digital twins, and robust anomaly-detection methods for reliable and sustainable system operation.
Harrou, F., Kini, K. R., Madakyaru, M., & Sun, Y. (2025). Sensor fault detection and diagnosis in photovoltaic systems using Hellinger Distance and Individual Conditional Expectation analysis. Solar Energy, 298, 113633.
Harrou, F., Dairi, A., Dorbane, A., & Sun, Y. (2023). Energy consumption prediction in water treatment plants using deep learning with data augmentation. Results in Engineering, 20, 101428.
Harrou, F., Cheng, T., Sun, Y., Leiknes, T., & Ghaffour, N. (2020). A data-driven soft sensor to forecast energy consumption in wastewater treatment plants: A case study. IEEE Sensors Journal, 21(4), 4908–4917.
Harrou, F., Dairi, A., Sun, Y., & Senouci, M. (2018). Statistical monitoring of a wastewater treatment plant: A case study. Journal of Environmental Management, 223, 807–814.
Harrou, F., Sun, Y., Hering, A. S., Madakyaru, M., & Dairi, A. (2020). Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications. Elsevier.
Ph.D. in Systems Optimization and Security, University of Technology of Troyes (UTT), France, 2010
Master’s in Information, Systems and Technology, University of Paris XI, France, 2006
Senior Research Scientist at KAUST with nearly 20 years of experience in artificial intelligence, anomaly detection, process monitoring, and predictive modeling. His work combines methodological innovation with real-world engineering applications, emphasizing robust, explainable, and deployable data-driven solutions. He has extensive experience in interdisciplinary research, scientific leadership, and research-industry collaboration.
Biological and Environmental Science and Engineering