Bio

I started my university study in Bologna, Italy, where I obtained a bachelor degree and a master in Energy Engineering. After winning a grant, I had the chance to develop my master thesis project within an international research group at Ecole Centrale de Paris. After this experience I publish my first paperand I became passionate about research, scientific writing and computational methods in general. It did not take long before I won a second grant for a Master of Research and a PhD. The grant covered 1-year Master at Liverpool University in the field ‘Decision Making under Risk and Uncertainty’ and 3-years of PhD in the same subject. My PhD is a multidisciplinary project bridging between engineering and computer science disciplines. During the 3 year PhD, I had the chance to attend several international conferences, publish 4 journal papers and work in direct contact with different research groups in different places in Europe (e.g. Politecnico di Milano and ETH Zurich). I developed a solid knowledge of uncertainty quantification method and probabilistic approaches. I also had the chance to work with novel and popular methods such as, for instance, Deep Reinforcement Learning and Bayesian updating methods.

Research Interests: Systems and Networks, Reliability, Vulnerability, Resilience, Machine Learning, Reinforcement Learning, Uncertainty Quantification, Sensitivity Analysis, Information Modelling, Simulations

Research project title: Enhance Smart Grid Reliability and Resilience

Project description: The aim of this project is to tackle problems related uncertainty and lack of data in the future grid (i.e. smart grid) resilience context. The effect of the interactions between power grid operations, its topological structure and external weather events are investigated. Power grid reliability, vulnerability and resilience concepts compared. The expected contribution is an efficient and effective computational framework which embeds a complex environmental-grid model resilience-reliability-vulnerability models. The newly developed frameworks will be capable of dealing with lack of reliable data and severe sources of uncertainty inevitably affecting the power grid system. The project is highly multidisciplinary, crossing knowledge from a variety of disciplines such as electrical engineering, reliability engineering, environmental science and computer science.

Supervisory team: Edoardo Patelli; Matteo Broggi; Sven Schewe

ORCID: 0000-0002-8117-8737