Research Team old

Federica Mandreoli

Full Professor

My research activity is in the field of data and information management and access. My current interests mainly focus on information sharing and exchange in heterogeneous and distributed contexts, stream data management, scalable data science, and data-centric AI. In the past, I worked on non-conventional data like textual information, tree-structured and graph-structured data, on temporal databases and schema versioning, and on personalized data access. My activities within these areas cover a wide spectrum, including theory and foundations, algorithm design, software and systems, and engagement with industry.

I’m a member of the Editorial Board of the following journals:

Associate Editor of Data Science and Engineering (Elservier)
Associate Editor of Frontiers in High-Performance Computing – section High Performance Big Data Systems
Review Editor of Frontiers in Artificial Intelligence – section AI in Business

This is the list of the most relevant recent professional services:

PC member of AAAI 2025, PC vice-chair of IEEE Big Data 2024, and PC chair of SEBD 2024
panelist at the panel entitled “Role of AI in Personalized Medicine and Drug Discovery” at the Open Innovations in Life Sciences (OILS) Conference 2024 organized by Life Science Zurich
speakers at the three-day symposium “Global Health in the Age of AI,” hosted by Fondazione Giorgio Cini, and organized by Prof. Luciano Floridi.

This is a selected list of my recent publications on these topics:

Davide Ferrari, Veronica Guidetti, Federica Mandreoli: Multi-Objective Symbolic Regression for Data-Driven Scoring System Management IEEE International Conference on Data Mining (ICDM), Orlando (FL), Dec. 2022. Open source code available on GitHub;
Fabio Grandi, Federica Mandreoli, Riccardo Martoglia, Wilma Penzo: Unleashing the power of querying streaming data in a temporal database world: A relational algebra approach. Information Systems 103: 101872 (2022);
Karl Hribernik, Giacomo Cabri, Federica Mandreoli, Gregoris Mentzas: Autonomous, context-aware, adaptive Digital Twins – State of the art and roadmap. Computers in Industry 133: 103508 (2021)

You can find the full list of my publications here, my DBLP entry here, and my Google Scholar profile here.

Veronica Guidetti

Postdoc Researcher

I’m a postdoctorate research fellow at the FIM Department of the University of Modena and Reggio Emilia. Within the FIM Department I collaborate closely with Professor F. Mandreoli, F. Motta and G. Buzzega. Our group is focused on Data Mining and Artificial Intelligence, and their applications in healthcare. I achieved a master (2016) and PhD degree (2021) in Theoretical Physics (FIS-02) at the University of Bologna. From October 2020 to November 2022, I was employed as a postdoc fellow at the Deutsches Elektronen-Synchrotron (DESY) in Hamburg, Germany. My detailed CV can be found at this link . – See more at: https://www.isgroup.unimore.it/members-veronica.html#sthash.uPrbpU2G.dpuf

My research at UniMoRe focuses on interpretable Machine Learning and its applications in high stakes domains, particularly in the medical field. Specifically, together with Professor Mandreoli (UniMoRe) and Davide Ferrari (King’s college, London) we have created a multi-objective symbolic regression method capable of creating and managing non-linear, intelligible…

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