Marco Parola
MLPI member
Download PDF version Up-to-date version of CV is available at https://MarcoParola.github.io/cv
Education
Pisa, Italy
Ph.D.
Ph.D. in Information Engineering enrolled in the XXXVIII cycle of the PhD in Information Engineering at University of Pisa. (in progress)
Pisa, Italy
Master degree
Master degree in Artificial Intelligence and Data Engineering at University of Pisa. Mark: 110/110
Pisa, Italy
Bachelor degree
Bachelor degree in Computer Engineering at University of Pisa. Mark: 97/110
Skills
Professional Experience
Clermont-Ferrant, France
Visiting Ph.D.
Visiting Ph.D. student at DREAM - Research on Embedded Architecture and Multisensor - research team at Insitut Pascal, Université Clermont Auvergne.
Deep Learning Patient monitoring Medical Imaging Computer Vision Video analysis
Aalborg, Denmark
Visiting Ph.D.
Visiting Ph.D. student at Visual Analysis and Perception Laboratory (vap-lab) at Aalborg University.
Deep Learning Transformers Thermal image Computer Vision Conditioning
Pisa, Italy
Research fellow
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.
Deep Learning XAI Medical Imaging Computer Vision
Lucca, Italy
Machine learning engineer
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.
Python Flask MongoDB Signal processing Sensors IoT microservices Docker
Livorno, Italy
Software developer
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.
.NET ASP.NET MSSQL C# web microservices
Pisa, Italy
Software developer intern
Software developer at Labortori Archa S.r.l.. Internship. Implementation of LIMS (Laboratory Information Management System) using Microsoft technology stack.
.NET ASP.NET MSSQL C# web
Publications
2024
Parola, M., Galatolo, F., La Mantia, G., 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
Object Detection Medical Imaging Deep Leraning CBR Oral Cancer
2023
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
Object Detection Medical Imaging Deep Leraning Ensemble Learning Oral Cancer
2023
2022
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.
Stuctural Health Monitoring Deep Leraning Sensors Digital Twin
2022
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
Stuctural Health Monitoring Deep Leraning Sensors Tower of Pisa
2022
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
Stuctural Health Monitoring Deep Leraning Sensors Digital Twin