People

I am an Associate Professor of Computer Science within the SySMA research unit of IMT Lucca. 

Currently, I am Research Associate to HPC Lab at ISTI, CNR, Pisa  

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.

https://sys.imtlucca.it/program-overview/software-quality-sq

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.)

• Machine Learning for Software analysis, University of Florence, Master Degree

• Machine Learning with Python, IMT Lucca, PhD

• Data Analysis and Management for Cultural Heritage, IMT Lucca, PhD  

• Manuel Pratelli, Cycle XXXVI, IMT Lucca

• Chiara Pugliese, Cycle XXXVII, University of Pisa

• Daisy Romanini, Cycle XXXVIII, IMT Lucca

• Marco Scapin, Cycle XXXVIII, IMT Lucca

• John Bianchi, Cycle XXXIX, IMT Lucca

• Giulio Loddi, Cycle XXXIX, IMT Lucca

• Luca Fantin, Cycle XLI, IMT Lucca

• 2025: Debora Giovannelli – Master student Data Science – University of Firenze, Final Thesis

• 2025: Samuele Miglietta – Master student Data Science – University of Firenze, Final Thesis

• 2025: Dario Comanducci – Master student Data Science – University of Firenze, Final Thesis

• 2024: Antonio Coschera – Master student Data Science – University of Firenze, Final Thesis

• 2024: Alessandro Lo Verde – Master student Data Science – University of Firenze, Final Thesis

• 2023: Luca Pennella – Master student Data Science – University of Firenze, Final Thesis

• 2023: Francesco Caso – Master student Data Science – University of Firenze, Final Thesis

• 2023: Andrea De Ranieri – Master student Data Science – University of Firenze, Final Thesis

• 2023: Giulio Loddi – Master student Data Science – University of Firenze, Final Thesis

• 2023: Eugenio Bucarelli – Master student Data Science – University of Firenze, Final Thesis

• 2023: Alessandro Mannolini – Master student Data Science – University of Firenze, Final Thesis

• 2022: Nikhil Suresh Thazhathethil – M.SC Computer Science – University of La Sapienza, Thesis

• 2022: Alessio Piccione – Undergraduate Computer Science – University of La Sapienza, Thesis

• 2022: Andrea Giusti – Undergraduate Computer Science – University of Pisa, Thesis

• 2021: Martino Ottolini – Master student Data Science – University of Firenze, Final Thesis

• 2021: Stefano Galiani – Master student Data Science – University of Firenze, Final Thesis

• 2021: Matteo Saccani – Master student Data Science – University of Firenze, Final Thesis

• L. Pennella, P. Saggese, F. Pinelli, L. Galletta. A unified framework and comparative study of decentralized finance derivatives protocols. Electronic Markets, 36(1), 70, 2026.

• J. Bianchi, M. Pratelli, F. Pinelli, M. Petrocchi. News source profiling from articles text: a benchmark study. Companion Publication of the 18th ACM Web Science Conference (WebSci), 2026.

• I. Sánchez Rodríguez, J. Bianchi, F. Pinelli, F. Panizza, E. Ricciardi, P. Pietrini. Understanding mental health discourse on Reddit with transformers and explainability. Scientific Reports, 16(1), 2026.

• D. Giovannelli, F. Pinelli, C. Pugliese. Mobile traffic data as a proxy for urban mobility: a preliminary study in Paris. EDBT/ICDT Workshops, 2026.

• M. Scapin, F. Pinelli, L. Galletta. ParserHunter: identify parsing functions in binary code. Journal of Systems and Software, 235, 2026.

• G. Loddi, C. Pugliese, F. Lettich, F. Pinelli, C. Renso. Urban region embeddings from service-specific mobile traffic data. 26th IEEE International Conference on Mobile Data Management (MDM), 2025.

• M. Pratelli, J. Bianchi, F. Pinelli, M. Petrocchi. Evaluation of reliability criteria for news publishers with large language models. Proceedings of the 17th ACM Web Science Conference (WebSci), 2025.

• L. Pennella, F. Pinelli, L. Galletta. X-SPIDE: an explainable machine learning pipeline for detecting smart Ponzi contracts in Ethereum. IEEE Access, 13, 2025.

• G. Loddi, A. Betti, F. Pinelli. A conditional generative diffusion model for spatio-temporal data. ECAI, Frontiers in Artificial Intelligence and Applications, 2025.

• L. Mazzoni, F. Pinelli, M. Riccaboni. Measuring corporate digital divide through websites: insights from Italian firms. EPJ Data Science, 2024.

• 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. Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC), 2024.

• J. Bianchi, M. Pratelli, M. Petrocchi, F. Pinelli. Evaluating trustworthiness of online news publishers via article classification. Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC), 2024.

• C. Pugliese, F. Lettich, F. Pinelli, C. Renso. Summarizing trajectories using semantically enriched geographical context. ACM 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 (SAC), 515–517, 2023.

• G. Costa, F. Pinelli, S. Soderi, G. Tolomei. Turning federated learning systems into covert channels. IEEE Access, 10, 130642–130656, 2022.

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

• M. Scapin, F. Pinelli, L. Galletta. ParserHunter: identify parsing functions in binary code. Journal of Systems and Software, 235, 2026.

• G. Loddi, A. Betti, F. Pinelli. A conditional generative diffusion model for spatio-temporal data. ECAI, Frontiers in Artificial Intelligence and Applications, 2025.

• L. Mazzoni, F. Pinelli, M. Riccaboni. Measuring corporate digital divide through websites: insights from Italian firms. EPJ Data Science, 2024.

• 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.