I am Associate Professor of Computer Science within the SySMA research unit of IMT Lucca. I worked as Research Scientists at IBM Research, Ireland, Dublin, then I held senior data scientist positions in Tiscali, Cloud4Wi and Vodafone. I received my Ph.D. in Computer Science from the University of Pisa, in 2010, and during my PhD I was visiting researcher at Senseable City Lab, at M.I.T., Cambridge, MA, US.

Research Area

My research interests are Data Mining and Machine Learning, and their application on different domains. On my early scientific career I have mainly focused on the development of Data Mining frameworks for spatio-temporal data to be applied on Urban Dynamics, and Intelligent Transportation systems. In the most recent years, I have worked on machine learning pipelines for business and marketing problems.

Currently, I am working on:

• Deep learning methods on mobile phone sensors data (e.g., GPS trajectories, Human Activity, etc. )
• Security on Federated Learning frameworks
• Trusthwortiness of news
• Applied machine learning (e.g., economics, blockchain, etc.)

Recent Publication

• C Pugliese, F Lettich, F Pinelli, C Renso, Summarizing Trajectories Using Semantically Enriched Geographical Context. SIGSPATIAL 2023

• F Lettich, C Pugliese, C Renso, F Pinelli. Semantic Enrichment of Mobility Data: A Comprehensive Methodology and the MAT-BUILDER System IEEE Access, 2023

• F Lettich, C Pugliese, C Renso, F Pinelli. A general methodology for building multiple aspect trajectories Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 515-517

• G Costa, F Pinelli, S Soderi, G Tolomei. Turning Federated Learning Systems Into Covert Channels IEEE Access 10, 130642-130656

• C Pugliese, F Lettich, C Renso, F Pinelli. Mat-builder: a system to build semantically enriched trajectories 2022 23rd IEEE International Conference on Mobile Data Management (MDM), 274-277, 2, 2022

Selected Publication

• F Calabrese, E Cobelli, V Ferraiuolo, G Misseri, F Pinelli, D Rodriguez. Using Vodafone mobile phone network data to provide insights into citizens mobility in Italy during the Coronavirus outbreak Data & Policy 3, e224, 2021

• F. Pinelli, R. Nair, F. Calabrese, G. Di Lorenzo, M. L. Sbodio, and M. Berlingerio. Data-driven transit network design from mobile phone trajectories. IEEE Transactions on Intelligent Transportation Systems, 2016.

• G. Di Lorenzo, M., F. Calabrese, M. Berlingerio, F. Pinelli, and R. Nair. Allaboard: Visual exploration of cellphone mobility data to optimise public transport. IEEE Transactions on Visualization and Computer Graphics, 2016.

• Y. Dong, F. Pinelli, Y. Gkoufas, Z. Nabi, F. Calabrese, and N. V. Chawla. Inferring unusual crowd events from mobile phone call detail records. ECML/PKDD, 2015.

• E. Diaz-Aviles, F. Pinelli, K. Lynch, Z. Nabi, Y. Gkoufas, E. Bouillet, F.Calabrese, E. Coughlan, P. Holland, and J. Salzwedel. Towards real-time customer experience prediction for telecommunication operators. IEEE International Conference on Big Data, 2015.

• F. Pinelli, F. Calabrese, and E. Bouillet. A methodology for denoising and generating bus infrastructure data, IEEE Transactions on Intelligent Transportation Systems, 2014.

• A. Monreale, D. Pedreschi, R. G. Pensa, and F. Pinelli. Anonymity preserving sequential pattern mining. Artificial Intelligence and Law, 2014.

• M. Berlingerio, F. Pinelli, F. Calabrese. Abacus: frequent pattern mining-based community discovery in multidimensional networks. Data Mining and Knowledge Discovery, 2013.

• F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, C. Renso, S. Rinzivillo, R. Trasarti. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal, 2011.

• R. Trasarti, F. Pinelli, M. Nanni, and F. Giannotti. Mining mobility user profiles for car pooling. ACM SIGKDD, 2011.

• A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. Wherenext: a location predictor on trajectory pattern mining. ACM SIGKDD, 2009.

• M. Berlingerio, F. Pinelli, M. Nanni, and F. Giannotti. Temporal mining for interactive workflow data analysis. ACM SIGKDD, 2009.

• F. Pinelli, A. H., F. Calabrese, M. Nanni, C. Zegras, and C. Ratti. Space and time-dependant bus accessibility: a case study in Rome. In IEEE International Conference on Intelligent Transportation Systems, ITSC, 2009.

• F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi. Trajectory pattern mining. ACM SIGKDD, 2007.

See my Google Scholar profile or my DBLP page for a full list of publications.

Patents

• Francesco Calabrese and Fabio Pinelli. Public transportation fare evasion inference using personal mobility data, 2014.
• Adi Botea, Michele Berlingerio, Eric Bouillet, Francesco Calabrese, and Fabio Pinelli. System for inferring inconvenient traveller experience in journeys, 2013.
• R Nair, F Pinelli, and F Calabrese. Real-time system to predict and correct scheduled service bunching, 2013.
• E Bouillet, F Calabrese, F Pinelli, M Sinn, and J Yoon. Estimation of arrival times at transit stops, 2012.
• E Bouillet, F Calabrese, F Pinelli, and O Verscheure. De-noising scheduled transportation data, 2012.