Laura Azzimonti

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

Check also the most up-to-date list on my Google Scholar profile.

International journal papers

  • 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]
  • G. Delfanti, F. Cortesi, A. Perini, G. Antonini, L. Azzimonti, C. de Lalla, C. Garavaglia, M.L. Squadrito, M. Fedeli, M. Consonni, S. Sesana, F. Re, H., Shen, P. Dellabona, G. Casorati: “TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells.” Science immunology, 7(74), eabn6563, 2022. [link]
  • D. Ravasi, F. Mangili, D. Huber, L. Azzimonti, L. Engeler, N. Vermes, G. Del Rio, V. Guidi, M. Tonolla, E. Flacio: “Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito Aedes albopictus in Switzerland.” International journal of environmental research and public health, 19(6), 3220, 2022. [link]
  • P. Berjano, F. Langella, L. Ventriglia, D. Compagnone, P. Barletta, D. Huber, F. Mangili, G. Licandro, F. Galbusera, A. Cina, T. Bassani, C. Lamartina, L. Scaramuzzo, R. Bassani, M. Brayda-Bruno, J.H. Villafañe, L. Monti, L. Azzimonti: “The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach.” Journal of Personalised Medicine, vol. 11(12):1377, 2021. [link]
  • F. Bini, A. Pica, L. Azzimonti, A. Giusti, L. Ruinelli, F. Marinozzi, P. Trimboli: “Artificial intelligence in thyroid field. A comprehensive review.” Cancers, vol. 13(19), 2021. [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]
  • F. Gorini, L. Azzimonti, G. Delfanti, L. Scarfó, C. Scielzo, M.T. Bertilaccio, P. Ranghetti, A. Gulino, C. Doglioni, A. Di Napoli, M. Capri, C. Franceschi, F. Calligaris-Cappio, P. Ghia, M. Bellone, P. Dellabona, G. Casorati, C. de Lalla: “Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis”, Blood, vol. 129, no. 26, 3440-3451, 2017. [link]
  • C. Cruder, D. Falla, F. Mangili, L. Azzimonti, L.S. Araùjo, A. Williamon, M. Barbero: “Profiling the location and extent of musicians’ pain using digital pain drawings”, Pain Practice, 2017. [link]
  • B. Guerciotti, C. Vergara, L. Azzimonti, L. Forzenigo, A. Buora, P. Biondetti, M. Domanin: “Computational study of the fluid-dynamics in carotids before and after endarterectomy”, Journal of Biomechanics, vol. 49, 26–38, 2016. [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, M.A. Cremona, A. Ghiglietti, F. Ieva, A. Menafoglio, A. Pini, P. Zanini: “BARCAMP: Technology Foresights and Statistics for the Future” in “Advances in Complex Data Modeling and Computational Methods in Statistics - Contributions to Statistics”, Springer, eds: A.M. Paganoni, P. Secchi, 53–67, 2014.
  • L. Azzimonti, F. Ieva, A.M. Paganoni: “A new unsupervised classification technique through nonlinear non parametric mixed effects models” in “Complex Models and Computational Methods in Statistics - Contributions to Statistics”, Springer, eds: Grigoletto, Lisi, Petrone, 1–11, 2013
  • 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, data]
  • C. de Lalla, A. Rinaldi, D. Montagna, L. Azzimonti, M.E. Bernardo, L.M. Sangalli, A.M. Paganoni, R. Maccario, A. Di Cesare-Merlone, M. Zecca, F. Locatelli, P. Dellabona, G. Casorati: “Invariant Natural Killer T-cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and associates with the remission state”, The Journal of Immunology, vol. 186, no. 7, 4490–4499, 2011. [link]

Conference proceedings

  • 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: “Structure learning from related data sets with a hierarchical Bayesian score”, Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR, 138, 5-16, 2020. [link]
  • L. Azzimonti, G. Corani, M. Zaffalon: “Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables” , IEEE 17th International Conference on Data Mining (ICDM), 2017. [link, code]
  • E. Arnone, L. Azzimonti, F. Nobile, L. Sangalli: “A time-dependent PDE regularization to model functional data defined over spatio-temporal domains” in “Functional Statistics and Related Fields”, Springer International Publishing, eds: G. Aneiros, E.G. Bongiorno, R. Cao, P. Vieu, 41–44, 2017. [link]
  • L. Azzimonti, L.M. Sangalli, P. Secchi: “Modeling prior knowledge on complex phenomena behaviors via partial differential equations”, Proceedings of the 47th Scientific Meeting of the Italian Statistical Society 2014, Cagliari, June 11-13, 2014. [link]
  • L. Azzimonti, L.M. Sangalli, P. Secchi: “Spatial regression with PDE penalization: an application to blood velocity field estimation”, Proceedings of the 8th conference on statistical computation and complex systems, Milano, September 9-11, 2012. [link]
  • L. Azzimonti, L.M. Sangalli, P. Secchi, M. Domanin: “PDE penalization for spatial fields smoothing”, Proceedings of the 46th Scientific Meeting of the Italian Statistical Society 2012, Rome, June 20-22, 2012. [link]
  • L. Azzimonti, F. Ieva, A.M. Paganoni: “A new unsupervised classification algorithm for nonlinear non parametric mixed effects models”, Proceedings of the 7th conference on statistical computation and complex systems, Padova, September 19-21, 2011. [link]
  • L. Azzimonti, M. Domanin, L.M. Sangalli, P. Secchi: “Surface estimation via spatial spline mod- els with PDE penalization”, Proceedings of the 7th conference on statistical computation and com- plex systems, Padova, September 19-21, 2011. [link]
  • L. Azzimonti, C. de Lalla, D. Montagna, A.M. Paganoni, L.M. Sangalli: “Mixed-effects models for growth curves: an application to the study of reconstitution kinetics of lymphocyte subpopulations”, Proceedings of the 45th Scientific Meeting of the Italian Statistical Society 2010, Padova, June 16-18, 2010. [link]

Thesis

L. Azzimonti: “Blood flow velocity field estimation via spatial regression with PDE penalization”, Ph.D. Thesis, Politecnico di Milano, 2013. [link]