In questa pagina è possibile consultare un elenco (non esaustivo) delle più recenti pubblicazioni dei docenti del master MD2SL sui temi della Data Science, di particolare rilievo e per ulteriore approfondimento rispetto agli argomenti trattati durante il programma.

Le pubblicazioni sono elencate in ordine alfabetico per titolo.


  • G. Aletti, I. Crimaldi, F. Saracco, “A model for the Twitter sentiment curve", PLoS ONE 16(4): e0249634 (2021)

  • G. Gnecco, “An algorithm for curve identification in the presence of curve intersections”, Mathematical Problems in Engineering, vol. 2018, article ID, 7243691, 7 pages, 2018. Hindawi, USA, ISSN: 1563-5147

  • G. Bet, M. Fischer, "An algorithm to construct subsolutions of convex optimal control problems", Submitted, 2021+. link arXiv

  • S. Baldassarri, G. Bet, "Asymptotic normality of degree counts in a general preferential attachment model", Submitted, 2021+. link arXiv

  • K. Kolykhalova, G. Gnecco, M. Sanguineti, G. Volpe, A. Camurri, “Automated analysis of the origin of movement: an approach based on cooperative games on graphs”, IEEE Transactions on Human-Machine Systems, vol. 50, pp. 550-560, 2020. IEEE, USA, ISSN: 2168-2291

  • F. J. Bargagli Stoffi, G. Gnecco, “Causal tree with instrumental variable: an extension of the causal tree framework to irregular assignment mechanisms”, International Journal of Data Science and Analytics, vol. 9, pp. 315-337, 2020. Springer, Germany, ISSN: 2364-415X

  • G. Caldarelli, R. De Nicola, M. Petrocchi, F. Saracco, “Chapter 12: In- formation Spreading and the Role of Automated Accounts on Twitter: Two Case Studies", in the book “Democracy and Fake News Information Manipulation and Post-Truth Politics", edited by Serena Giusti and Elisa Piras for Routledge (Taylor and Francis group)

  • C. Becatti, I. Crimaldi, F. Saracco, “Collaboration and follower- ship: a stochastic model for activities in bipartite social networks”, PLOS ONE 14(10): e0223768 (2019)

  • A. Bacigalupo, G. Gnecco, M. Lepidi, L. Gambarotta, “Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization”, Computer Methods in Applied Mechanics and Engineering, vol. 375, article no. 113623, 22 pages, 2021. Elsevier, Netherlands, ISSN: 0045-7825

  • G. Bet, K. Bogerd, R. M. Castro, R. van der Hofstad, "Detecting a botnet in a network", Accepted for publication in Mathematical Statistics and Learning, 2021. link arXiv

  • C. Becatti, G. Caldarelli and F. Saracco, “Entropy-based randomisation of rating networks”, Phys. Rev. E 99, 022306 (2019)

  • C. Becatti, G. Caldarelli, R. Lambiotte and F. Saracco, “Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections”, Palgrave Communications 5, 91 (2019)

  • A. Bacigalupo, G. Gnecco, M. Lepidi, L. Gambarotta, “Machine-learning techniques for the optimal design of acoustic metamaterials”, Journal of Optimization Theory and Applications, vol. 187, pp. 630-653, 2020. Springer, Germany, ISSN: 0022-3239

  • R. Morisi, D. N. Manners, G. Gnecco, N. Lanconelli, C. Testa, S. Evangelisti, L. Talozzi, L. L. Gramegna, C. Bianchini, G. Calandra-Bonaura, L. Sambati, G. Giannini, P. Cortelli, C. Tonon, R. Lodi, “Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines”, Parkinsonism & Related Disorders, vol. 47, pp. 64-70, 2018. Elsevier, Netherlands, ISSN: 1353-8020

  • P. Cinat, G. Gnecco, M. Paggi, “Multi-scale surface roughness optimization through genetic algorithms”, Frontiers in Mechanical Engineering, vol. 6, article no. 29, 14 pages, 2020. Frontiers, Switzerland, ISSN: 2297-3079

  • R. Zoppoli, M. Sanguineti, G. Gnecco, T. Parisini, “Neural approximations for optimal control and decision”, 518 pages, 2020. Springer, Switzerland, series “Communications and Control Engineering”. ISBN: 978-3-030-29691-9.

  • G. Gnecco, M. Sanguineti, “Neural approximations in discounted infinite-horizon stochastic optimal control problems”, Engineering Applications of Artificial Intelligence, vol. 72, pp. 294-302, 2018. Elsevier, Netherlands, ISSN: 0952-1976

  • G. Gnecco, F. Nutarelli, “On the trade-off between number of examples and precision of supervision in machine learning problems”, Optimization Letters, 2019, DOI: 10.1007/s11590-019-01486-x. Springer, Germany, ISSN: 1862-4472

  • G. Gnecco, F. Nutarelli, D. Selvi, “Optimal trade-off between sample size and precision for the fixed effects generalized least squares panel data model”, Machine Learning, 2021, forthcoming. Springer, Germany, ISSN: 0085-6125

  • G. Gnecco, F. Nutarelli, D. Selvi, “Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model”, Soft Computing, vol. 24, pp. 15937-15949, 2020. Springer, Germany, ISSN: 1432-7643

  • P. Lenarda, G. Gnecco, M. Riccaboni, “Parameter estimation in a 3-parameter p* random graph model”, Networks, 2020, DOI: 10.1002/net.21992. Wiley, USA, ISSN:1097-0037

  • G. Gnecco, “Symmetric and antisymmetric properties of solutions to kernel-based machine learning problems”, Neurocomputing, vol. 306, pp. 141-159, 2018. Elsevier, Netherlands, ISSN: 0925-2312

  • G. Caldarelli, R. De Nicola, F. Del Vigna, M. Petrocchi and F. Saracco, “The role of bot squads in the political propaganda on Twitter", Communications Physics volume 3, Article number: 81 (2020)