Summary
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 and latent factor models, including topic modelling. 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.
Selected Publications
Journal Articles
Sanna Passino F, Che Y, Cardoso Correia Perello C, 2024, Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems, Stat, Vol:13, ISSN:2049-1573
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
Conference
September MAK, Sanna Passino F, Goldmann L, et al., Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks, 27th International Conference on Artificial Intelligence and Statistics (AISTATS)