Marco Parola

Ph.D. in Information Engineering enrolled in the XXXVIII cycle of the PhD in Information Engineering at University of Pisa. (in progress)

Master degree in Artificial Intelligence and Data Engineering at University of Pisa. Mark: 110/110

Visiting Ph.D. student at Visual Analysis and Perception Laboratory (vap-lab) at Aalborg University.

Ph.D. candidate enrolled in Information Engineering phd program at University of Pisa.

Research fellow on "Decision support systems with explainable models for diagnosis and prognosis based on medical images" funded by MUR-PRIN REASONIG 2020 project, supervisors Prof. Gigliola Vaglini and Prof. Mario GCA Cimino; at University of Pisa.

Machine learning engineer at Move Solutions - Move S.r.l.. Part time. I developed microservices to be integrated into the Move Solutions platform architecture to provide ML analysis on sensor signals.

Software developer at 5Space S.n.c.. I worked part-time on OptGear project, a web application to perform optimization calculations for mechanical gears, which finds the best combination of gear parameters.

Parola, M., La Mantia, G., Galatolo, F., Cimino, M.G., Campisi, G., Di Fede, O., 2023. Image-based screening of oral cancer via deep ensemble architecture, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. pages 1572–1578. doi:10.13140/RG.2.2.24070.34880

Simionato, G.; Parola, M.; and Cimino, M.; (2023). Impressionist Hole Detection and Healing Using Swarms of Agents with Quantized Perception. In Proceedings of 2023 IEEE Symposium Series on Computational Intelligence. (2023). pages 1213–1220

Parola, M.; Dirrhami, H.; Cimino, M. and Squeglia, N. (2023). Effects of Environmental Conditions on Historic Buildings: Interpretable Versus Accurate Exploratory Data Analysis. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 429-435. DOI: 10.5220/0012119700003541

Parola, M.; Galatolo, F.; Torzoni, M. and Cimino, M. (2022). Convolutional Neural Networks for Structural Damage Localization on Digital Twins. In International Conference on Deep Learning Theory and Applications (pp. 78-97). Cham: Springer Nature Switzerland.

Cimino, M.; Galatolo, F.; Parola, M.; Perilli, N. and Squeglia, N. (2022). Deep Learning of Structural Changes in Historical Buildings: The Case Study of the Pisa Tower. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 396-403. DOI: 10.5220/0011551800003332

Parola, M.; Galatolo, F.; Torzoni, M.; Cimino, M. and Vaglini, G. (2022). Structural Damage Localization via Deep Learning and IoT Enabled Digital Twin. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-584-5; ISSN 2184-9277, SciTePress, pages 199-206. DOI: 10.5220/0011320600003277