Attività didattiche

Corsi di laurea e dottorato

• Algoritmi per dati su larga scala @unimi laurea magistrale 2019-20 – 2020-21
• Algoritmi per dati su larga scala (DSE) @unimi laurea magistrale 2020-21
• Architectural partterns for distributed ML @unimi dottorato 2019-20
• Didattica dell'informatica @unimi laurea magistrale 2019-20
• Programmazione per l'analisi dei dati @uca DUT 2020-21 – 2021-22
• Statistica e analisi dei dati @unimi laurea 2019-20 – 2020-21

Ricerca

Tematiche di ricerca

• Induzione di insiemi fuzzy
• Compressione di modelli di machine learning
• Mining di basi di conoscenza nel Web semantico
• Selezione di esempi negativi in ambito bioinformatico
• Previsione del rischio COVID-19 basata su ML
• Applicazione di ML in ambito veterinario e forense
• Divulgazione della cultura informatica

Progetti

• Multicriteria Data Structures and Algorithms: from compressed to learned indexes, and beyond PRIN
• Fostering a correct view of informatics PSR

Pubblicazioni

[Casali et al., 2021] Casali Michelangelo, Malchiodi Dario, Spada Claudio, Zanaboni Anna M., Cotroneo Rosy, Furci Domenico, Sommariva Andrea, Genovese Umberto e Blandino Alberto A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles, Journal of Forensics and Legal Medicine 84 (2021), 102256 [doi> BIBTEX]

[Bagardi et al., 2021] Bagardi Mara, Locatelli Chiara, Zanaboni Anna M., Galizzi Alberto, Malchiodi Dario e Brambilla Paola G. Multiple retrospective analysis of survival and evaluation of cardiac death predictors in a population of dogs affected by degenerative mitral valve disease in ACVIM class C treated with different therapeutic protocols, Polish Journal of Veterinary Sciences 24 - 1 (2021), 109—118 [doi> BIBTEX]

[Esposito et al., 2021] Esposito Andrea A., Casiraghi Elena, Chiaraviglio Francesca, Scarabelli Alice, Stellato Elvira, Plensich Guido, Lastella Giulia, Di Meglio Letizia, Fusco Stefano, Avola Emanuele, Jachetti Alessandro, Giannitto Caterina, Malchiodi Dario, Frasca Marco, Beheshti Afshin, Robinson Peter N., Valentini Giorgio, Forzenigo Laura e Carrafiello Gianpaolo Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical, and a Qualitative Chest X-Ray Scoring System, Reports in Medical Imaging 14 (2021), 27—39 [doi> BIBTEX]

[Galizzi et al., 2021] Galizzi Alberto, Bagardi Mara, Stranieri Angelica, Zanaboni Anna M., Malchiodi Dario, Borromeo Vitaliano, Brambilla Paola G. e Locatelli Chiara Factors affecting the urinary aldosterone-to-creatinine ratio in healthy dogs and dogs with naturally occurring myxomatous mitral valve disease, BMC Veterinary Research 17 - 1 (2021), 1—14 [doi> BIBTEX]

[Marinò et al., 2021] Marinò Giosuè C., Ghidoli Gregorio, Frasca Marco e Malchiodi Dario Reproducing the sparse Huffman Address Map compression for deep neural networks, in B. Kerautret, M. Colom, A. Krähenbühl, Adrien, D. Lopresti, P. Monasse e H. Talbot (Eds.), Reproducible Research in Pattern Recognition, Cham: Springer International Publishing, Lecture Notes in Computer Science 12636, 161—166, 2021 [doi> BIBTEX]

[Marinò et al., 2021a] Marinò Giosué C., Ghidoli Gregorio, Frasca Marco e Malchiodi Dario Compression strategies and space-conscious representations for deep neural networks, in 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 9835—9842, 2021 [doi> BIBTEX]

[Casiraghi et al., 2020] Casiraghi Elena, Malchiodi Dario, Trucco Gabriella, Frasca Marco, Cappelletti Luca, Fontana Tommaso, Esposito Andrea A., Avola Emanuele, Jachetti Alessandro, Reese Justin, Rizzi Alessandro, Robinson Peter N. e Valentini Giorgio Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments, IEEE Access 8 (2020), 196299—196325 [doi> BIBTEX]

[Malchiodi et al., 2020] Malchiodi Dario, da Costa Pereira Célia e Tettamanzi Andrea G. Classifying Candidate Axioms via Dimensionality Reduction Techniques, in V. Torra, Y. Narukawa, J. Nin e N. Agell (Eds.), Modeling Decisions for Artificial Intelligence. 17th International Conference, MDAI 2020 Sant Cugat, Spain, September 2–4, 2020 Proceedings, Cham, Switzerland: Springer, Lecture Notes in Computer Sciencce 12256, 179—191, 2020 [doi> BIBTEX]

[Cermenati et al., 2020] Cermenati Luca, Malchiodi Dario e Zanaboni Anna Maria Simultaneous Learning of Fuzzy Sets, in A. Esposito, M. Faundez-Zanuy, M. Morabito e E. Pasero (Eds.), Neural Approaches to Dynamics of Signal Exchanges, Vol. 151, Singapore: Springer, Smart Innovation, Systems and Technologies, 167-175, 2020 [doi> BIBTEX]

[Lodi et al., 2019] Lodi Michael, Malchiodi Dario, Monga Mattia, Morpurgo Anna e Spieler Bernadette Constructionist Attempts at Supporting the Learning of Computer Programming: A Survey, Olympiads in Informatics 13 (2019), 99—121 [doi> BIBTEX]

[Malchiodi and Zanaboni, 2019] Malchiodi Dario e Zanaboni Anna Maria Data-Driven Induction of Shadowed Sets Based on Grade of Fuzziness, in R. Fullér, S. Giove e F. Masulli (Eds.), Fuzzy Logic and Applications — 12th International Workshop, WILF 2018 Genoa, Italy, September 6–7, 2018 — Revised Selected Papers, Cham: Springer Nature Switzerland AG, Lecture Notes in Artificial Intelligence 11291 (ISBN 978-3-030-12543-1/978-3-030-12544-8), 17—28, 2019 [doi> BIBTEX]

[Malchiodi et al., 2018] Malchiodi Dario, da Costa Pereira Célia e Tettamanzi Andrea G. Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression, in D. Ciucci, G. Pasi e B. Vantaggi (Eds.), Scalable Uncertainty Management. SUM 2018, Cham: Springer, Lecture Notes in Artificial Intelligence 11142 (ISBN 978-3-030-00460-6/978-3-030-00461-3), 2018 [doi> BIBTEX]

[Boldi et al., 2018] Boldi Paolo, Frasca Marco e Malchiodi Dario Evaluating the impact of topological protein features on the negative examples selection, BMC Bioinformatics 19 - 14 (2018), 417.115–417.126 [doi> Open access link BIBTEX]

[Malchiodi and Tettamanzi, 2018] Malchiodi Dario e Tettamanzi Andrea G. Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering, in H. Haddad, R. L. Wainwright e R. Chbeir (Eds.), SAC'18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, ACM (ISBN 9781450351911), 1984–1991, 2018 [doi> BIBTEX]

[Malchiodi and Pedrycz, 2013] Malchiodi Dario e Pedrycz Witold Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering, in F. Masulli, G. Pasi e R. Yager (Eds.), Fuzzy Logic and Applications. 10th International Workshop, WILF 2013, Genoa, Italy, November 19–22, 2013. Proceedings., Vol. 8256, Springer International Publishing, Switzerland, Lecture Notes on Artificial Intelligence (ISBN 978-3-319-03199-6), 52–59, 2013 [doi> BIBTEX]