Federica Baccini

Federica Baccini is an Assistant Professor at the Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome. She is actively involved in the RSTLess research group, collaborating with Professors Fabrizio Silvestri and Irene Amerini. Her academic journey includes a PhD in Computer Science from the University of Pisa and a Master’s in Applied Mathematics from the University of Siena.

Her educational background includes:

• PhD in Computer Science, University of Pisa, thesis: Analysis of Multiple Relations in Multilayer and Higher-Order Networks, supervised by Prof. Monica Bianchini and Dr. Filippo Geraci.
• Master’s Degree in Applied Mathematics, University of Siena, summa cum laude, thesis: Network analysis for the Integration of histone modification data to explain haematopoiesis.
• Visiting PhD student at Queen Mary University of London under Prof. Ginestra Bianconi.

Federica's research is centered on the analysis of graph-structured data, particularly focusing on multilayer and higher-order networks. She investigates machine learning models for graphs, with emphasis on similarity network fusion, weighted simplicial complexes, and data integration across disciplines. Her work spans applications in biomedicine, environmental science, scientometrics, and AI security. She has published in high-impact journals such as Physical Review E, Journal of Informetrics, and Mathematics.

The recent trend in her publications highlights a strong interdisciplinary approach, combining network science with machine learning to solve real-world problems in healthcare, scholarly communication, and climate systems. Her 2025 work on adversarial data poisoning shows engagement with AI security, while her medical applications (e.g., haemodialysis app, IgA nephropathy) demonstrate translational research impact.

She has no listed scientific awards at this time.

Federica is actively involved in academic advising and teaching, offering the course Fundamentals of Artificial Intelligence in the Bachelor's program in Mathematical Sciences for Artificial Intelligence. While specific grant details are not mentioned, her ongoing research in AI, network science, and interdisciplinary applications suggests active participation in funded projects. She is a member of the Theory of Deep Learning initiative.

She is a key contributor to the RSTLess research group, where she collaborates on cutting-edge topics in deep learning and network analysis.