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Marco Frasca is an Associate Professor of Computer Science at the University of Milan, where he is member of the Computational Biology and Bioinformatics Lab (AnacletoLab). He holds a Ph.D. (2012) in Computer Science from the University of Milan - Milan, Italy. Before his current appointment, he served the The Department of Biosciences of University of Milan as a Lecturer. He has held short-term research visits at Royal Holloway University, London, UK (2019), the IMB of Johannes Gutenberg University, Mainz, Germany (2017), and the Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada (2015). He is a Member of the CINI National Laboratory in Artificial Intelligence and Intelligent Systems (AIIS), of the CINI National Laboratory CINI of Big Data, and of the Bioinformatics Italian Society (BITS).

His primary research interests include Artificial Intelligence for Cryptocurrency, Biology & Medicine, particularly Deep Learning; Deep Neural Networks Compression; Learned Indexes; Learning with Imbalanced Data.

Biography » CV (PDF)»

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Selected Publications All Publications»

h-index: 13   Citations: 929   Erdös #: 3   ORCID   Scopus  

• IEEE ACCESS'23 "On Nonlinear Learned String Indexing", Ferragina, P., Frasca, M., Marino, G.C., and Vinciguerra, G. IEEE Access 2023, 11, pages 74021-74034.
• NEUROCOMPUTING'23 "Deep neural networks compression: A comparative survey and choice recommendations", Marinò, G.C., Petrini, A., Malchiodi, D., Frasca, M. Neurocomputing 2023, 520, pages 152-170.
• ICPRAM'22 "On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters", Fumagalli, G., Raimondi, D., Giancarlo, R., Malchiodi, D. and Frasca, M. InProceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 675-682.
• OUP'21 "HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction", M. Notaro, M. Frasca, A. Petrini, J. Gliozzo, E. Casiraghi, P.N. Robinson and G. Valentini. Bioinformatics, 2021, 37(23):4526-4533.
• SCIREP'20 "Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction", J. Gliozzo, P. Perlasca, M. Mesiti, E. Casiraghi, V. Vallacchi, E. Vergani, M. Frasca, G. Grossi, A. Petrini, M. Re, A. Paccanaro and G. Valentini, Scientific Reports, 10:3612, 2020.
• IEEE ICPR'20 "Compression strategies and space-conscious representations for deep neural networks", G. Marino', G. Ghidoli, M. Frasca, D. Malchiodi, 25th International Conference on Pattern Recognition (ICPR 20), 2020, pp. 9835-9842.
• PONE'20 "Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools", P. Perlasca, M. Frasca, C.T. Ba, J. Gliozzo, M. Notaro, M. Pennacchioni, G. Valentini, M. Mesiti, PLoS ONE, 15(12): e0244241. 2020.
• ECML'19 "Multitask Hopfield Networks", M. Frasca, G. Grossi and G. Valentini, in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg16 September 2019.
• AIM'17 "Gene2DisCo:Gene to disease using disease commonalities", M. Frasca, Artificial Intelligence in Medicine, 82:34-46, 2017.
• IEEE/ACM'17 "Multitask protein function prediction through task dissimilarity", M. Frasca and N. Cesa Bianchi, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(5), 2017.
• JCB'15 "Unbalance-aware Network Integration and Prediction of protein functions", M. Frasca, A. Bertoni, G. Valentini, Journal of Computational Biology, 22(12) 1057–1074, 2015.
• NCA'15 "Learning node labels with multi-category Hopfield networks", M. Frasca, S.Bassis, G. Valentini, Neural Computing and Applications, 27(6): 1677-1692, 2015.
• NN'13 "A neural network algorithm for semi-supervised node label learning from unbalanced data", M. Frasca, A. Bertoni, M. Re, and G. Valentini, Neural Networks, 43, pp.84-98, July 2013.
• ECML'11 " COSNet: a Cost Sensitive Neural Network for Semi-supervised Learning in Graphs", A. Bertoni, M. Frasca, G. Valentini, in Proceedings of the European Conference on Machine Learning, ECML PKDD, 2011, Athens, 5-9 September 2011.

All Publications »

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Courses More »

• MLDNN23 Machine Learning, Artificial Neural Networks and Deep Learning (Spring)
• LASD22 Algorithms and Data Structures (Winter)
• INFOSTAT22 Informatics and Statistics
• LASD21 Algorithms and Data Structures (Winter)
• INFOSTAT21 Informatics and Statistics
• LASD20 Algorithms and Data Structures (Winter)
• INFOSTAT20 Informatics and Statistics
• LASD19 Algorithms and Data Structures (Winter)
• INFOSTAT19 Informatics and Statistics
• LDB18 Laboratory of Databases
• DMML18 Data Mining and Machine Learning
• LDB17 Laboratory of Databases
• LASD13-17 Algorithms and Data Structures

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Software

• sHAM Sparse Huffman Address Map (Deep Neural Networks Compression)
• UNIPredWeb UNIPred-Web (A web tool for the integration and visualization of biomolecular networks for protein function prediction)
• COSNet COSNet:Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings (Hopfield Network, Cost-senstivie Learning, Recurrent Neural Network)

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Professional Service More »

Editorial Boards

• Guest Editor, Data Mining and Knowledge Discovery, journal track of ECML/PKDD 19-21. Journal, Editor-in-Chief: Johannes Fürnkranz, Springer Nature Switzerland (06/2019 - current)
  
• Guest Editor, Machine Learning, journal track of ECML/PKDD 19-21. Journal, Editor-in-Chief: Hendrik Blockeel, Springer Nature Switzerland (06/2019 - current)
  

• Associate Editor, International Journal of Neural Networks, journal track of ECML/PKDD 19-21. Journal, Editor-in-Chief: Qiaolin Ye, Bioinfo Publication, Kharghar Navi Mumbai (01/2013 - 01/2017)

Conference Committees

• ICBRA: 2023 [International Conference on Bioinformatics Research and Applications]
• ICANN: 2020 [International Conference on Artificial Neural Networks]
• IEEE MLSP: 2019, 2018, 2017, 2016 [IEEE's International Workshop on Machine Learning for Signal Processing]
• IEEE ICASSP: 2019 [IEEE's International Conference on Acoustics, Speech and Signal Processing]
• IJCNN: 2013 [International Joint Conference of Neural Network, session chair]

Journal Referee

• Journal Referee: Springer's Scientific Reports, IEEE TNNLS, BioMed Central's BMC Bioinformatics, Elsevier's Neural Networks, Elsevier's Neurocomputing, Elsevier's Information Sciences

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Other Links

• Multicriteria Data Structures PRIN Project, I am the UNIMI Coordinator.
• LinkedIn My LinkedIn
• ResearchGate My ReasearchGate
• BITS I am member of BITS
• Alberto In memory of Alberto Bertoni, my PhD supervisor
Tennis My favorite hobby :-)

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Contact

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