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Dario Malchiodi

Università degli Studi di Milano unimi
(associate professor)
INRIA + UCA INRIA + UCA
(visiting scientist)
Université de la Côte d'Azur uca
(visiting professor)
Data Science Research Centre DSRC
(scientific board)


News
01/04/2021 teaching AMD SAD
Office hours on April, 2nd
The office hours of April, 2nd are canceled.
30/03/2021 teaching AMD SAD
Access to recorded lectures
Access to the recorded lectures is allowed only to students who have signed up in the course moodle page. Should this not be possible, students are asked to contact the teacher.
10/03/2021 teaching AMD-DSE
Availablity of recorded lectures of «Algorithms for massive datasets» for the Master in Data Science for Economics
The videorecorded lectures of the course «Algorithms for massive datasets» for the Master in Data Science for Economics will be available until the end of March.
10/03/2021 teaching AMD
Office hours change for the course «Algorithms for massive datasets»
The 11/03 office hours of the course «Algorithms for massive datasets» will start at 18:30. From next week office hours will take place every Friday at 16:30.
Older news

Teaching activities


Bachelor, master and PhD

  • Algorithms for massive data (DSE) @unimi master Lectures are in English 2020-21
  • Algorithms for massive datasets @unimi master Lectures are in English 2019-202020-21
  • Architectural partterns for distributed ML @unimi PhD Lectures are in English 2019-20
  • Computer programming for data analysis @uca DUT Lectures are in french 2019-202020-21
  • Computing education @unimi master Lectures are in italian 2019-20
  • Statistics and data analytics @unimi bachelor Lectures are in italian 2019-202020-21
All activities

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
All areas

Projects

All projects

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]

All publications