Angela Andreella

Assistant Professor at Ca’ Foscari University of Venice

Universiy of Padua

I am an Assistant Professor at the Department of Economics of the University Ca’ Foscari Venezia. I am analyzing and using big data to redefine the design of services to citizens and communities to customize and adapt interventions to the characteristics of the territories and communities of reference.

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. We are studying Procrustes-based methods in the context of high-dimensional data’s functional alignment like fMRI, EEG, and genetic data. In particular, we are developing an extension of the famous perturbation model to improve interpretability, inferential proprieties, model flexibility, and optimization of the computational effort. We are also working on modeling big clinical data collaborating with Dr. Sabrina Brigadoi.

I also finished 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.

Mainly, my interest is to connect the clinical and statistical fields improving both areas. For that, I am a member of the Psicostat group at the University of Padova.

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. I am collaborating with 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 of 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 lower bounds for each cluster’s number of active voxels. The method can be applied as many times as the user want, still controlling the family-wise error rate.

I am also collaborating with Paolo Brambilla and Giuseppe Delvecchio on various psychiatrical and psychological projects.

Curriculum Vitae


  • Dimensional reduction techniques
  • High Dimensional Statistic
  • Multiple Testing and Selective Inference
  • Permutation test
  • Statistical Shape Analysis
  • Statistic applied in Neuroscience


  • PhD in Statistical Science, 2017

    University of Padua

  • MSc in Statistical Science, 2016

    University of Padua

  • BSc in Statistical Science, 2013

    University of Padua