Domenico Tortorella received the PhD in computer science cum laude from the University of Pisa, Italy, in 2024 for his thesis “Efficient Models for Deep Learning on Graphs” under the supervision of prof. Alessio Micheli. Previously he received the BSc in computer engineering from the University of Salerno, Italy, in 2017, and the MSc in computer science from the University of Pisa, Italy, in 2020, both cum laude. Currently, he is an assistant professor (RTD-A) in the Department of Computer Science at the University of Pisa, and he is a member of the Computational Intelligence and Machine Learning research group (CIML). He currently hold the office of Secretary of the European Neural Network Society (ENNS).

Research topics

My research interests include machine learning on structured data, with a particular focus on neural networks for sequences and graphs, reservoir computing, and explainable AI. I have contributed to the development of efficient neural architectures for graphs and the theoretical analysis of their properties, with applications in bioinformatics and cheminformatics.

Read more here about the topics:

• Machine learning for graphs
• Reservoir computing
• Explainable AI

Upcoming conferences

• 15th IAPR-TC15 International Workshop Graph-Based Representations in Pattern Recognition (GbR 2027) ↪

Recent publications

Re-evaluating Out-of-Distribution Learning in Drug Discovery

F. Capria, A. Micheli, D. Tortorella (2027). "Re-evaluating Out-of-Distribution Learning in Drug Discovery." AIxIA 2026 – Advances in Artificial Intelligence. AIxIA 2026, LNCS vol. 17131, pp. 143-154.

Graph Learning Models for Temporal Gene Expression Prediction and the Role of Interactions Topology

A. Dipalma, A. Micheli, P. Milazzo, F. Simonetti, D. Tortorella (2027). "Graph Learning Models for Temporal Gene Expression Prediction and the Role of Interactions Topology." Computational Methods in Systems Biology. CMSB 2026, LNCS vol. 16678, pp. 334-350.

A method for the systematic generation of graph XAI benchmarks via Weisfeiler–Leman coloring

M. Fontanesi, A. Micheli, M. Podda, D. Tortorella (2026). "A method for the systematic generation of graph XAI benchmarks via Weisfeiler–Leman coloring." Data Mining and Knowledge Discovery, vol. 40(4), 42.

Contact

Room 330
Department of Computer Science, University of Pisa
Largo B. Pontecorvo, 3, 56127 Pisa, Italy