Roberto Rocchetta

Bio

Roberto Rocchetta is a distinguished researcher within the Intelligent Energy Systems group at SUPSI-DACD-ISAAC. He holds a Master's degree in Energy Engineering from the University of Bologna (Italy) and a Ph.D. in Reliability Engineering from the University of Liverpool (UK). His career includes significant tenures at renowned institutions such as NIA/NASA-Langley (Virginia, USA) and TU Eindhoven. Roberto's research expertise lies in decision-making under risk and uncertainty, intelligent energy systems management and optimization, uncertainty quantification, and reliability/risk-based design. His multidisciplinary approach integrates concepts from system reliability engineering, statistical learning theory, stochastic optimization, uncertainty quantification, and machine learning. Roberto has contributed more than 15 peer-reviewed journal articles and his work (2018-2024), garnered over 900 citations, underscoring its significant impact on the academic and research community.

Academic Background, Visiting & Placements

I hold a Master and a Bachelor degree in Energy Engineering from the University of Bologna (Italy), a Master of Research in Decision-making Under Risk and Uncertainty from the University of Liverpool. I received my Ph.D. from the University of Liverpool, United Kingdom 2019, while doing research at the Institute for Risk and Uncertainty and School of Engineering. My dissertation dealt with the development of computational frameworks for the analysis of resilience, reliability and vulnerability analysis of power grid systems (Download Dissertation Here). During my Ph.D. and Masters I was a visiting student at ETH Zurich, Switzerland, and at Polytechnic of Milano, Italy, and at Ecole Central de Paris, France, where I spent two five and six months, respectively. I worked in the start-up group ARAMIS s.r.l.in Milan Italy, where I helped developing Machine Learning (Deep Reinforcement Learning) framework to tackle health management and maintenance problems.

Research Areas & Expertise

My research focuses on developing advanced uncertainty quantification framework for the analysis of complex systems and critical infrastructures. I have experience with optimization under uncertainty and reliability-based design-optimization (RBDO). More recently, I developed anomaly detection methods based on SVM ensembles equipped with formal generalization error bounds. I am also interested in several machine learning methods applicable to anomaly detection, remaining useful life estimation and tasks related to the topic "Prognostics and Health Management". During my PhD, my research focused on power networks resilience and reliability. Specifically, I developed computational tools for the analysis/identification of risks and criticalities based on topological analysis of the network structure and physics-based simulation of the network reaction to critical failures (e.g. cascading events, targeted attacks).

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