Latest paper: 'Design of supervision solutions for industrial equipment: Schemes, tools and guidelines for the user', published in the Journal of Industrial Information Integration; 'A Note on the Numerical Solutions of Kernel-Based Learning Problems', published in IEEE Transactions on Automatic Control, 2021; 'Kernel-based identification of asymptotically stable continuous-time linear dynamical systems', published in International Journal of Control, 2022.
Research Experience
Presented at the 32th workshop on European Research Network System Identification (ERNSI); Co-founder of AISent srl, a company dedicated to creating impactful solutions for the industry through technology.
Education
Specific educational details are not provided.
Background
Associate Professor of Automatic Control at the Department of Management, Information and Production Engineering, University of Bergamo, Italy. Research interests include the development of theory and practice of system identification and supervision algorithms for dynamical systems, as well as the intersection of data science, signal processing, and machine learning. Currently focused on: kernel methods for system identification, signal processing on graphs, analysis of vibration signals, robust and data-driven fault detection, processes for developing supervision solutions, prognostics with scenario-based optimization approaches.
Miscellany
Blog posts cover topics such as visualizing classification results, human perception of probability, and the three ways of statistical inference.