People

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

• 1st Workshop on  Generative AI for Mobility Data @MDM'24 

• Doctoral Colloquium Chair @MDM'24 

My research interests are Data Mining and Machine Learning and their application in different domains. In my early scientific career, I have mainly focused on the development of Data Mining frameworks for spatiotemporal data to be applied to 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 sensor data (e.g., GPS trajectories, Human Activity, etc. )

• Security on Federated Learning Frameworks

• Trustworthiness of news

• Applied machine learning (e.g., economics, blockchain, etc.)

• C. Pugliese, F. Lettich, F. Pinelli, C. Renso. Understanding Human Mobility Dynamics: Insights from Summarized Semantic Trajectories, 25th IEEE International Conference on Mobile Data Management (MDM), 2024 (short paper)

• L.  Galletta, F. Pinelli. Explainable Ponzi Schemes Detection on Ethereum, 39th ACM/SIGAPP Symposium on Applied Computing

• J. Bianchi, M. Pratelli, M. Petrocchi, F. Pinelli. Evaluating Trustworthiness of Online News Publishers via Article Classification, 39th ACM/SIGAPP Symposium on Applied Computing

• 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

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

• 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.

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

• F. Calabrese and F. Pinelli. Public transportation fare evasion inference using personal mobility data, 2014.

• A. Botea, M. Berlingerio, E. Bouillet, F. Calabrese, and F. 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.