Laura Azzimonti

Senior Researcher and Lecturer at SUPSI-IDSIA, Lugano, Switzerland and SIB Group Leader.

Main interests

My main research interests are probabilistic graphical models, hierarchical Bayesian modelling, physics-informed machine learning, federated learning and the application of machine learning techniques to personalised medicine.

Some of the applied projects in the biomedical field are described at the IDSIA health website.

Here is a list of selected papers.

Selected papers

  • M. Scutari, C. Marquis, L. Azzimonti: “Using mixed-effects models to learn Bayesian networks from related data sets”, Proceedings of the 11th International Conference on Probabilistic Graphical Models, PMLR, 186, 73-84, 2022. [link]
  • L. Azzimonti, G. Corani, M. Scutari: “A Bayesian Hierarchical score for structure learning from related data sets.” International Journal of Approximate Reasoning, col. 142, 248-265, 2022. [link]
  • K. Sechidis, L. Azzimonti, A. Pocock, G. Corani, A. Weatherall, G. Brown: “Efficient feature selection using shrinkage estimators”, Machine Learning Journal, vol. 108, 1261-1286, 2019. [link]
  • L. Azzimonti, G. Corani, M. Zaffalon: “Hierarchical estimation of parameters in Bayesian networks”, Computational Statistics and Data Analysis, vol. 137, 67-91, 2019. [link, code]
  • E. Arnone, L. Azzimonti, F. Nobile, L.M. Sangalli: “Modeling spatially dependent functional data via regression with differential regularization”, Journal of Multivariate Analysis, vol. 170, 275 - 295, 2019. [link]
  • L. Azzimonti, L.M. Sangalli, P. Secchi, M. Domanin, F. Nobile: “Blood flow velocity field estimation via spatial regression with PDE penalization”, Journal of the American Statistical Association, Theory and Methods Section, vol. 110, no. 511, 1057–1071, 2015. [link, code]
  • L. Azzimonti, F. Nobile, L.M. Sangalli, P. Secchi: “Mixed finite elements for spatial regression with PDE penalization”, SIAM/ASA Journal on Uncertainty Quantification, vol. 2, 305–335, 2014. [link]
  • L. Azzimonti, F. Ieva, A.M. Paganoni: “Nonlinear nonparametric mixed-effects models for unsupervised classification”, Computational Statistics, vol. 28, no. 4, 1549–1570, 2013. [link, code]