Francesco Sanna Passino

Francesco Sanna Passino is an Assistant Professor in Statistics at Imperial College London's Department of Mathematics, within the Faculty of Natural Sciences. His research focuses on statistical analysis of dynamic networks, Bayesian methods, latent variable models, and applications in cybersecurity, social networks, and transportation systems. He holds a PhD in Statistics from Imperial College London (2021), an MSc in Statistics from Imperial College London (2017), and dual BSc degrees in Statistics from the University of Glasgow (2016) and the University of Bologna (2016).

Research interests span dynamic network analysis, event-time data, latent factor models, and model-based clustering. Notable applications include statistical cybersecurity (e.g., attack pattern detection), social network dynamics, and infrastructure systems like bike-sharing. His work integrates advanced statistical techniques such as latent variable models, point processes, and spectral embedding.

Publications emphasize methodological innovations in network time series, synthetic data generation, and graph-based processes. Collaborations include Imperial's Artificial Intelligence Network. No awards are explicitly listed, but his research aligns with cutting-edge topics like AI-driven financial trend detection and privacy-preserving synthetic data.

He advises no listed students and has no documented grants in the provided texts. His work is housed in the Huxley Building on the South Kensington Campus. For further details, visit his Imperial webpage or Google Scholar profile.