2025-2026

Franco Maria Nardini (http://hpc.isti.cnr.it/∼nardini/) is currently a Researcher at ISTI–CNR in Pisa. He received his Ph.D. in Information Engineering from the University of Pisa in 2011 discussing his thesis ``Query Log Mining to Enhance User Experience in Search Engines''. His research interests are focused on Web Information Retrieval, Data Mining, and Machine Learning. Franco Maria Nardini is member of the program committee of important conferences in IR and DM like ACM CIKM, SIGKDD, WSDM, etc.

He is Professor of Computer Science at Scuola Superiore Sant'Anna. He leads the Advanced Algorithms and Applications Laboratory (Acube Lab, http://acube.di.unipi.it), at the Department of Computer Science, whose main research activities are devoted to the design, analysis and experimentation of algorithms and data structures for storing, compressing, mining and retrieving information from Big textual collections and labeled graphs.

Researcher at the CNR-IRPPS. Previously, she served as a Research Fellow at the CNR Institute of Information Science and Technologies “A. Faedo” and was a PhD candidate in the National Doctoral Program in Artificial Intelligence and Society, collaborating with the University of Pisa and the University of Bari “Aldo Moro.” Her research focused on Human-Centered Artificial Intelligence, Explainable AI (XAI), and Visual Analytics. She developed a particular interest in XAI interfaces and the role of Visual Analytics in improving the usability of explainable AI systems.

Francesca Naretto is a researcher at the University of Pisa. She holds a PhD in Data Science from the Scuola Normale Superiore, where she worked on the intersection of explainability and privacy in machine learning. Her research interests are in the field of ethical artificial intelligence, focusing on privacy, fairness, and federated learning. She studies how these principles can be integrated into the design of trustworthy AI systems that are transparent, accountable and compliant with the regulations.

Cosimo Rulli is a researcher at the National Research Council (CNR) in Italy. He obtained his PhD in 2023 from the University of Pisa, with a thesis on deep neural network compression. His research interests include Deep Learning, Model Compression, and Approximate Nearest Neighbor techniques, particularly applied to Information Retrieval. He was awarded the ACM SIGIR 2024 Best Paper Runner-Up prize and the ECIR 2025 Best Student Short Paper award. He is a reviewer for ACM TOIS, IEEE TKDE, and PMC, and serves on the program committee for SIGIR, ECIR, CIKM, and WSDM.

Lorenzo Bellomo studied Computer Science at the University of Pisa, where he earned his degree with a thesis entitled “Topic Based News Recommendation”. He later obtained a Ph.D. in Data Science from the Scuola Normale Superiore in Pisa, with a dissertation titled “Building a Biomedical Knowledge Graph from PubMed Central Articles”. In the following years, he carried out research for the Computer Science departement of the University of Pisa on data compression and graph databases, often in collaboration with the company Sadas S.r.l.

Francesco Spinnato earned his Bachelor's degree in Economics and Management from the University of Padua in 2017 and his Master's degree in Data Science from the University of Pisa in 2020. He obtained his Ph.D. in Data Science from the Scuola Normale Superiore in 2024.

Andrea Cossu is assistant professor at the Computer Science department of the University of Pisa. His research revolves around continual learning and non-stationary environments, with applications to pre-trained models ad recurrent neural networks. Andrea was Board Member of ContinualAI, a non-profit research organization on continual learning, and is one of the maintainers of the Avalanche library. Andrea holds a Ph.D. in Data Science from Scuola Normale Superiore, he was visiting researcher at KU Leuven and Research Intern at Google Brain. 

Alessandro Poggiali is a Ph.D. student in Artificial Intelligence at the University of Pisa, researching hybrid quantum algorithms for clustering, feature selection, and anomaly detection. He earned his Bachelor’s and Master’s degrees in Computer Science at the same university. He has collaborated with the Fraunhofer IKS in Munich on quantum autoencoders and is active in teaching and outreach, co-organizing the Quantum Festival.  His current research aims to develop more efficient and interpretable models in quantum artificial intelligence.