About Fouzi Harrou Fouzi Harrou Senior Research Scientist, Statistics spatio-temporal statistics Environmental Statistics statistics machine learning Harrou’s research focuses on statistical decision theory and its applications, multivariate statistical process monitoring, anomaly detection and diagnosis. Events Presented Events Feb 8 - Feb 14, 2026 Data-driven Anomaly Detection in Industrial Processes Fouzi Harrou, Senior Research Scientist, Statistics Feb 12, 12:00 - 13:00 B9 L2 R2325 anomaly detection multivariate statistics artificial intelligence AI This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques. May 7 - May 13, 2023 Process Monitoring Techniques and Applications Fouzi Harrou, Senior Research Scientist, Statistics May 8, 12:00 - 13:00 B9 L3 R3128 Abstract Effective process monitoring is crucial to ensure engineering and environmental plants' reliable and safe operation. If anomalies in engineering or environmental plants are not detected promptly, they can affect plants' productivity, profitability, and safety. Both model-based and data-based process monitoring techniques have proven themselves in practice over the past four decades. This talk will present a model-based approach for anomaly detection in a linear model with a bounded nuisance parameter and a data-based process monitoring example using machine learning. Three real-world
Data-driven Anomaly Detection in Industrial Processes Fouzi Harrou, Senior Research Scientist, Statistics Feb 12, 12:00 - 13:00 B9 L2 R2325 anomaly detection multivariate statistics artificial intelligence AI This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.
Process Monitoring Techniques and Applications Fouzi Harrou, Senior Research Scientist, Statistics May 8, 12:00 - 13:00 B9 L3 R3128 Abstract Effective process monitoring is crucial to ensure engineering and environmental plants' reliable and safe operation. If anomalies in engineering or environmental plants are not detected promptly, they can affect plants' productivity, profitability, and safety. Both model-based and data-based process monitoring techniques have proven themselves in practice over the past four decades. This talk will present a model-based approach for anomaly detection in a linear model with a bounded nuisance parameter and a data-based process monitoring example using machine learning. Three real-world
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