Paolo Bonetti is a post-doctoral researcher in the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano.
His research focuses on machine learning for climate science, with an emphasis on interpretable methods, extreme event and drought detection, dimensionality reduction, causality and information flow, and reinforcement learning control. He earned a PhD in Information Technology from Politecnico di Milano (awarded cum laude; defended April 2025), where he developed interpretable and causal methodologies for meaningful variable extraction and their application to climate extreme events. He previously received both a BSc and an MSc in Mathematical Engineering (Statistical Learning) from Politecnico di Milano, and was an Erasmus exchange student at Aalto University. His main research goal is to innovate artificial intelligence methods and applications while preserving impact and practical applicability and interpretability, bridging methodological advances with real-world needs and constraints.