I am a Lecturer in Statistics in the Department of Mathematics at Imperial College London. 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.
For further details, see my personal webpage.
Sanna Passino F, Heard N, 2022, Latent structure blockmodels for Bayesian spectral graph clustering, Statistics and Computing, ISSN:0960-3174
Sanna Passino F, Heard NA, Rubin-Delanchy P, 2021, Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel, Technometrics, ISSN:0040-1706
Sanna Passino F, Turcotte MJM, Heard NA, 2021, Graph link prediction in computer networks using Poisson matrix factorisation, Annals of Applied Statistics, ISSN:1932-6157
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
et al., 2021, Where To Next? A Dynamic Model of User Preferences, WWW '21: The Web Conference 2021, ACM