On this page it is possible to browse a (non-exhaustive) list of the most recent publications on Data Science by the lecturers of the MD2SL master, especially relevant and for further study on the topics covered during the program.

Publications are listed alphabetically by title.


  • 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)