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.
For further details, see my personal webpage.
I am always interested in hearing from potential PhD students. If you think that your interests and background are a good match with my research, please contact me. For more details, please see the Supervision tab on my personal webpage.
Sanna Passino F, Heard NA, 2022, Mutually exciting point process graphs for modelling dynamic networks, Journal of Computational and Graphical Statistics, ISSN:1061-8600
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
et al., 2021, Where To Next? A Dynamic Model of User Preferences, WWW '21: The Web Conference 2021, ACM