Teaching activities

Bachelor, master and PhD

• Algorithms for massive data (DSE) @unimi master 2020-21
• Algorithms for massive datasets @unimi master 2019-20 – 2020-21
• Architectural partterns for distributed ML @unimi PhD 2019-20
• Computer programming for data analysis @uca DUT 2019-20 – 2020-21
• Computing education @unimi master 2019-20
• Statistics and data analytics @unimi bachelor 2019-20 – 2020-21

Research

Research areas

• Data-driven induction of fuzzy sets
• Compression of machine learning models
• Mining of knowledge bases in semantic Web
• Negative example selection in bioinformatics
• ML-based COVID-19 risk prediction
• Application of ML in veterinary and forensics
• Popularization of informatics culture

Projects

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

Publications

[Esposito et al., 2021] Esposito A. A., Casiraghi E., Chiaraviglio F., Scarabelli A., Stellato E., Plensich G., Lastella G., Di Meglio L., Fusco S., Avola E., Jachetti A., Giannitto C., Malchiodi D., Frasca M., Beheshti A., Robinson P. N., Valentini G., Forzenigo L. and Carrafiello G. 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 A., Bagardi M., Stranieri A., Zanaboni A. Maria, Malchiodi D., Borromeo V., Brambilla P. G. and Locatelli C. 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 G., Ghidoli G., Frasca M. and Malchiodi D. Reproducing the sparse Huffman Address Map compression for deep neural networks, in B. Kerautret, M. Colom, A. Krähenbühl, D. Lopresti, P. Monasse and H. Talbot (Eds.), Proceedings of the third Workshop on Reproducible Research in Pattern Recognition RRPR2021, Springer, Lecture Notes in Computer Science, 2021, in press [BIBTEX]

[Marinò et al., 2021a] Marinò G. C., Ghidoli G., Frasca M. and Malchiodi D. Compression strategies and space-conscious representations for deep neural networks, in Proceedings of the 25th International Conference on Pattern Recognition (ICPR2020), IEEE, 9835—9842, 2021 [BIBTEX]

[Casiraghi et al., 2020] Casiraghi E., Malchiodi D., Trucco G., Frasca M., Cappelletti L., Fontana T., Esposito A. A., Avola E., Jachetti A., Reese J., Rizzi A., Robinson P. N. and Valentini G. 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 D., da Costa Pereira C. and Tettamanzi A. G. B. Classifying Candidate Axioms via Dimensionality Reduction Techniques, in V. Torra, Y. Narukawa, J. Nin and 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 L., Malchiodi D. and Zanaboni A. Simultaneous Learning of Fuzzy Sets, in A. Esposito, M. Faundez-Zanuy, M. Morabito and 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 M., Malchiodi D., Monga M., Morpurgo A. and Spieler B. 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 D. and Zanaboni A. Data-Driven Induction of Shadowed Sets Based on Grade of Fuzziness, in R. Fullér, S. Giove and 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 D., da Costa Pereira C. and Tettamanzi A. G. B. Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression, in D. Ciucci, G. Pasi and 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 P., Frasca M. and Malchiodi D. 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 D. and Tettamanzi A. G. B. Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering, in H. Haddad, R. L. Wainwright and 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 D. and Pedrycz W. Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering, in F. Masulli, G. Pasi and 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]