Francesco Passino

I am a Lecturer in Statistics in the Department of Mathematics at Imperial College London (roughly equivalent to Assistant Professor). My main research interests are broadly based on statistical analysis of dynamic networks. In my work, I enjoy exploring an array of different statistical techniques, adapted and extended to dynamic network modelling, such as latent variable models and model-based clustering.

In recent years, I have also developed an interest for statistical analysis of event-time data, topic modelling, Bayesian non-parametric methods, and recommender systems. My research has been mainly applied to statistical cyber-security problems, but also to social networks, music streaming services, and bike sharing systems.

Selected Publications

Journal Articles

• Sanna Passino F, Heard NA, 2023, Mutually exciting point process graphs for modelling dynamic networks, Journal of Computational and Graphical Statistics, Vol:32, ISSN:1061-8600, Pages:116-130
• Sanna Passino F, Heard N, 2022, Latent structure blockmodels for Bayesian spectral graph clustering, Statistics and Computing, Vol:32, ISSN:0960-3174
• Sanna Passino F, Heard NA, Rubin-Delanchy P, 2021, Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel, Technometrics, Vol:64, ISSN:0040-1706, Pages:346-357
• Sanna Passino F, Turcotte MJM, Heard NA, 2021, Graph link prediction in computer networks using Poisson matrix factorisation, Annals of Applied Statistics, Vol:16, ISSN:1932-6157, Pages:1313-1332
• Sanna Passino F, Heard N, 2020, Bayesian estimation of the latent dimension and communities in stochastic blockmodels, Statistics and Computing, Vol:30, ISSN:0960-3174, Pages:1291-1307
• Sanna Passino F, Maystre L, Moor D, et al., 2021, Where To Next? A Dynamic Model of User Preferences, WWW '21: The Web Conference 2021, ACM

Affiliations

• Artificial Intelligence Network
• Mathematics research and teaching staff
• Statistics

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