PhD student · Started in 2025
Alessandro PAGANI
Trustworthy Situational Understanding in Multivariate Time Series
CurriculumComputer Science/Engineering and Control Systems
AdvisorFederico Cerutti
Research groupTo be confirmed
Situational understanding can be defined as the ability to recognize what is happening, gain insight into why it is happening, and use that knowledge to anticipate what may happen next. We aim to apply this concept to time-series analysis, contributing to the literature on improving the interpretability and trustworthiness of AI systems.
Other advisors: Sergio Benini
Profile
Short Bio
I hold bachelor’s and master’s degrees in Computer Science and Engineering from the University of Brescia. My bachelor’s thesis focused on the virtualization of industrial LoRaWAN networks, while my master’s thesis involved implementing and analysing the behaviour of a Retrieval-Augmented Generation system. My research interests include machine learning and machine unlearning, mitigating opacity in deep learning models, and information representation.
