💡 Research interest

My main interest is applied statistics, i.e., connecting statistics with different fields, such as social sciences, health sciences (aging and fragility in particular), psychology, neuroscience, and medicine, improving both areas. My challenge is analyzing these data and related problems with proper and novel statistical approaches. For that, I am a member of the Psicostat group at the University of Padova, LoLa group, and a GBD (Italian division) collaborator.

My statistical interests are in methods that permit understanding and managing high-dimensional data, i.e., dimension reduction techniques, statistical shape analysis, permutation tests, multiple testing, and selective inference.

🐦 Something about me

I am currently an assistant professor in social statistics at the Department of Economics of the University Ca’ Foscari Venezia. I am working on various projects related to Italian social welfare and health data analysis.

Previously, I did a Post-Doc at the Department of Statistical Sciences of the University of Padova under the supervision of Professor Bruno Scarpa and Livio Finos. Here, we worked on sample size estimation in the power analysis context for PLS classification.

I also did a Post-Doc at the Department of Science and Technology of the University of Insubria with the supervision of Professor Antonietta Mira. The project was focused on modeling the data from emergency call centers across the Lombardy region in Italy.

I finished my Ph.D. in Statistical Sciences under the supervision of Professor Livio Finos about statistical methods to analyze neuroscience data, e.g., fMRI data.

One Ph.D. thesis project was about a spatial regularization of the Procrustes-based method used in the functional alignment of brain data, i.e., the ProMises (Procrustes von Mises-Fisher) model with the collaboration of Professor James Haxby from the Department of Psychological and Brain Sciences at Dartmouth College and with the Haxby Lab.

Another project of the Ph.D. thesis was about a permutation-based approach to the All Resolution Inference method under the supervision of Professor Jelle Goeman from the Department of Biomedical Data Sciences and Professor Wouter Weeda from the Department of Psychology at Leiden University. The method fixes the spatial specificity paradox, computing post-hoc lower bounds for the number of true active voxels within a cluster.

My cv is downloadable here for the English version and here for the Italian one.