People

Riccardo Guidotti (born in 1988 in Pitigliano, GR, Italy) is an Assistant Professor (RTD-B) at the Department of Computer Science at the University of Pisa and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a research group in collaboration with ISTI-CNR of Pisa and with Scuola Normale Superiore.

Riccardo Guidotti graduated with honors in Computer Science (Master’s and Bachelor’s degrees in 2013 and 2010, respectively) from the University of Pisa, where he also earned his Ph.D. in Computer Science with a thesis on “Personal Data Analytics”. He won the IBM fellowship program and worked at IBM Research Dublin, Ireland, in 2015, the DSAA New Generation Data Scientist Award in 2018, and the Marco Somalvico Award for Artificial Intelligence in 2021.

His research interests include explainable artificial intelligence, interpretable machine learning, personal data mining, clustering, and the analysis of transactional data and time series.

I am a Ph.D. candidate in Computer Science at the University of Pisa, supervised by Professors Riccardo Guidotti and Salvatore Ruggieri.

I graduated with a Master’s Degree in Data Science & Business Informatics from the University of Pisa in 2021, with a thesis on “Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery.” I also hold a Bachelor’s Degree in Digital Humanities from the same university, graduating with honors in 2017.

My research is primarily focused on the development of methodologies that discover and exploit causal knowledge to uncover and prevent discrimination and to promote the interpretability of ML models. Throughout my academic career, I have collaborated with institutions such as INRIA in France and Saarland University in Germany, working on advanced causal analysis techniques for fairness in AI and dynamic systems.

I am a Ph.D. candidate in AI within the national PhD Programme under the area AI & Society.

During my studies, I explored the relationship between technology and human rights. I work on Fairness and Explainability in Natural Language Processing, focusing on digital discrimination and algorithmic biases. I hold a Bachelor’s Degree in Digital Humanities from the University of Pisa, during which I developed a strong interest in hate speech detection towards minorities in online discourse.

Research Interests: NLP; Responsible Language Technologies; Human-Centered AI; Explanability; Transparency; Fairness; Algorithmic Auditing; ML Evaluation; Data Awareness; Intersectionality; Digital Discrimination; Abusive Language Detection

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.

I am also interested in Natural Language Processing tools, particularly in the detection of harmful online content. Additionally, I hold a Bachelor’s Degree in Philosophy from the University of Pisa, where I focused on philosophy of language, philosophical logic and epistemology.

Anna Monreale is an associate professor at the Computer Science Department of the University of Pisa and a member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group with the Information Science and Technology Institute of the National Research Council in Pisa.

Her research interests include big data analytics, social networks and the privacy issues raising in mining these kinds of social and human sensitive data. In particular, she is interested in the evaluation of privacy risks during analytical processes and in the design of privacy-by-design technologies in the era of big data.

She earned her Ph.D. in computer science from the University of Pisa in June 2011 and her dissertation was about privacy-by-design in data mining.

Mattia Setzu is a researcher at the Computer Science department of University of Pisa, and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab). His research focuses on explainable AI, with a focus on explanation of tabular and text data. Mattia graduated summa cum laude (2018), and then obtained his PhD in Computer Science (2022) with a thesis titled Opening the Black Box: Empowering Machine Learning Models with Explanations.

On explainability, he studies decision trees, their expressive power, and adaptation to non-relational data. On natural language processing, he worked on authorship attribution tasks, in which a text of an anonymous author must be assigned to a set of possible authors, and on fairness of language model, by studying possible causes of unfair behavior.

Francesco Spinnato earned his Bachelor’s degree in Economics and Management from the University of Padua in 2017, and he received 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.

He is currently a researcher at the University of Pisa. His research focuses on explainable AI (XAI) applied to sequential data, particularly on interpreting black-box models for univariate and multivariate time series. In 2024, he collaborated with the Visualization and Data Analytics (VIDA) Research Center at New York University.

Dr. Spinnato was a recipient of the 2023-2024 Socint G-Research Italian National Prize for his Ph.D. thesis: Explanation Methods for Sequential Data Models.

I am a Ph.D. student in the National Ph.D. in Artificial Intelligence for Society program at the University of Pisa. I hold a Master’s Degree in Data Science and Business Informatics from the University of Pisa and a Bachelor’s Degree in Computer Science from the University of Salerno.

My research focuses on developing human-centered AI approaches that emphasize explainability, interpretability, and trustworthiness. I am particularly interested in the interpretability of Few-Shot Learning models, where I develop both post-hoc explainers and interpretable-by-design architectures. Additionally, I am exploring the trustworthiness of AI systems in relation to the latest AI Act requirements.

Cristiano Landi (born in 1998 in Cecina, LI, Italy) is a Ph.D. student at the Department of Computer Science at the University of Pisa and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a research group in collaboration with ISTI-CNR of Pisa and Scuola Normale Superiore. Cristiano graduated with a Master’s Degree in Data Science & Business Informatics from the University of Pisa in 2022, with a thesis on “Interpretable Machine Learning Models for Trajectory Classification”. He also holds a Bachelor’s Degree in Computer Science from the same university, obtained in 2017.

Cristiano’s research focuses on developing alternative data representations to improve the performance of current state-of-the-art machine learning models. Throughout his academic career, he has collaborated with the Data Science Lab at the University of Piraeus, Greece, on “Mobility Data Representation for Sub-Trajectory Clustering.” His other research interests include Mobility Data Analytics, Explainable and Interpretable AI, and Human-Centered AI.

Alessandro Berti is a Post-hoc Researcher in Quantum Computing at University of Pisa. He loves innovation and technology and in 2019 started a podcast, the PointerPodcast, which deals with these two themes. In 2019, he started Tocket, a startup project about ticketing based on Blockchain technology whose aim is defeating scalping. In 2020, he built a community, Superhero Valley, whose target is to prepare students for interviews in big tech companies.

I am a PhD Student in Artificial Intelligence at the University of Pisa, currently enrolled in the Italian National PhD Program in AI.

My research interests focus on quantum machine learning. I hold a strong academic background in artificial intelligence, with a commitment to advancing the frontiers of quantum-enhanced AI.