I am Assistant Professor (RTDB) 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:

• Security on Federated Learning frameworks
• Deep learning for trajectory data
• Machine learning applied on economics data

Selected Publication

• 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.