The Statistics For Engineering Research (SFERe) group of the Department of Industrial Engineering of the University of Naples Federico II is dedicated to advancing statistical methodologies tailored to complex challenges in engineering and applied sciences. It consists of professors, post-docs, and Ph.D. students from the Department of Industrial Engineering at the University of Naples Federico II, working collaboratively to advance statistical methodologies in engineering research. Our mission is to bridge the gap between statistical theory and practical applications, driving innovation in the following areas:

• Statistical Process Monitoring: Developing advanced control charts and monitoring techniques for complex industrial processes, including multivariate and functional data approaches. We also address budget-constrained scenarios using active learning to ensure cost-effective monitoring in complex industrial systems.
• Functional Data Analysis (FDA): Applying FDA methods to model and analyze data that vary over a continuum, such as time or space. This includes functional regression models to predict and understand relationships in engineering data, as well as methods for clustering and classification to group similar data points and identify patterns or anomalies in engineering processes.
• Robust Statistical Methods: Creating robust techniques to handle outliers and model deviations in multivariate and functional data, ensuring reliable analysis in industrial settings.
• Generalized Additive Models (GAMs): Developing and applying GAMs for flexible and interpretable modeling of engineering data, with a focus on ensemble methods for probabilistic forecasting.
• Applications in Engineering Systems: Applying statistical methods to real-world engineering challenges, such as monitoring CO₂ emissions in maritime transport, monitoring railway systems, and optimizing manufacturing processes.