Andrea Alessandrelli holds a degree in Theoretical Physics and is currently a PhD candidate in the National PhD Program in Artificial Intelligence. His research focuses on modeling biological and artificial neural networks using methods from probability theory, statistics, and statistical mechanics of complex systems.
He is a researcher at the ISTI institute of CNR, Pisa. He holds a degree (1996) and a PhD (2001) in Computer Science, both obtained at the Pisa University. His research topics include data mining in general, clustering methods, mobility data analysis and their applications. He participated to several national and international projects, the most recent being PETRA, a UE project centered around mobility in a "smart cities" context. He has been a visiting in a few international institutes, including M.I.T. in Boston and University of New Brunswick in Canada.
Lorenzo Mannocci received the Phd.D. degree in the Italian National PhD in Artificial Intelligence at the University of Pisa and IIT-CNR. Currently, he is a Research Fellow on the european TANGO project on Hybrid Artificial Intelligence models for detection of (in)authentic behaviour and content. He is interested in the overlap between web science and data science, particularly in the study of bot detection and coordinated online behavior. He also conducts research in Explainable AI and Human-Computer Interaction, with a focus on using RAG-based systems.
Giacomo Fidone is a Ph.D. student in Artificial Intelligence at the Computer Science Department of the University of Pisa. His research interests primarily concern Natural Language Generation (NLG) and explainability, with a focus on Large Language Models (LLMs) and generative methods for inducing interpretable models. Before starting his Ph.D., he worked as a research fellow on the PIANO project, developing simulators of online social networks.
Andrea Iommi obtained a Master's Degree in Computer Science from the University of Pisa in 2024 with 110/110, specialising in Artificial Intelligence (AI). Currently, he is engaged in a PhD (National PhD for Society) at the same institution. His research focuses on topics such as Explainable Artificial Intelligence (XAI) and Ranking.
I am a Graduate Student of Digital Humanities from the University of Pisa and I am currently pursuing a PhD in the Italian National PhD Program in AI.
My research interests gravitate around explainable machine learning themes, with a specific focus on case-based reasoning approaches to developing interpretable-by-design models.
Andrea Macrì obtained his Bachelor’s degree in Economics (2017) and Master’s degree in Finance (2020) from the University of Siena. Since 2021, he has been a PhD candidate (XXVII cycle) in Computational Methods and Mathematical Models for Sciences and Finance, under the supervision of Professors F. Lillo and S. Marmi.
Andrea Failla is a PhD student in Artificial Intelligence for Society within the Italian National PhD Program in AI and a research fellow at the National Research Council of Italy, working in the Knowledge Discovery and Data Mining Laboratory (KDDLab). His research focuses on network science approaches to computational social science, with particular attention to higher-order and temporal network analysis. His recent work explores the opportunities and challenges of using Large Language Models in social science research.
Riccardo Massidda is a post-doctoral researcher at the Department of Computer science of the Università di Pisa. His research focuses on the intersection of machine learning and causality, focusing on causal abstraction methods. He completed his PhD as part of the ELLIS Society network, under the supervision of Davide Bacciu (University of Pisa) and Sara Magliacane (University of Amsterdam).
Marco Podda is an Assistant Professor in the Department of Computer Science at the University of Pisa, and he is a member of che Computational Intelligence & Machine Learning (CIML) group. He got his Ph.D. in Computer Science at the University of Pisa in 2021. His research interests are Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, and Generative Models applied to structured data such as sequences and graphs. His research finds application for the most part in the biomedical field.
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