Francesco Ferrini
Located in Trento, Italy
Hi there 🌱
I am Francesco Ferrini, a researcher in Artificial Intelligence and Machine Learning. I am currently pursuing a Ph.D. in Artificial Intelligence and Machine Learning at the Structured Machine Learning Group, University of Trento, Italy, under the supervision of Andrea Passerini (University of Trento) and Manfred Jaeger (University of Aalborg, Denmark).
My research focuses on Graph Neural Networks and relational learning, with particular interest in multi-relational and heterogeneous graph models, as well as learning paradigms that explicitly account for incomplete, uncertain, or imperfect information in real-world data.
During my Ph.D., I have also carried out a research period in Japan, where I worked at the National Institute of Advanced Industrial Science and Technology (AIST) in collaboration with academic and industrial researchers. This experience allowed me to further explore graph-based learning in applied and interdisciplinary settings, and to strengthen my international research profile.
Prior to my doctoral studies, I obtained a Master's degree in Artificial Intelligence Systems. During this period, I worked on several projects spanning Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing.
I also interned at Aalborg University in Denmark, where I began my Master's thesis research on meta-path learning for Graph Neural Networks in heterogeneous graphs.
Outside of research, I enjoy practicing sports—especially calisthenics and tennis—and playing chess.
Feel free to explore my website to learn more about my research, projects, and publications. If you are interested in collaboration, have a research idea, or would simply like to discuss science and technology, do not hesitate to get in touch.
- Contact me at francescoferrini1997@gmail.com
Publications
Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
Authors: Francesco Ferrini, Veronica Lachi, Antonio Longa, Bruno Lepri, Matono Akiyoshi, Andrea Passerini, Xin Liu, Manfred Jaeger
Type: arXiv Preprint
Year: 2026
Bridging Theory and Practice in Link Representation with Graph Neural Networks
Authors: Veronica Lachi*, Francesco Ferrini*, Antonio Longa, Bruno Lepri, Andrea Passerini, Manfred Jaeger
Conference: NeurIPS 2025
Accepted as: Spotlight
Year: 2025
GNNs Meet Sequence Models Along the Shortest Path: An Expressive Method for Link Prediction
Authors: Francesco Ferrini*, Veronica Lachi*, Antonio Longa, Bruno Lepri, Andrea Passerini
Workshop: NeurIPS 2025 – New Perspectives in Graph Machine Learning
Year: 2025
Beyond Sparse Benchmarks: Evaluating GNNs with Realistic Missing Features
Authors: Francesco Ferrini, Veronica Lachi, Antonio Longa, Bruno Lepri, Andrea Passerini, Xin Liu, Manfred Jaeger
Workshop: NeurIPS 2025 – New Perspectives in Graph Machine Learning
Accepted as: Oral
Year: 2025
A Self-Explainable Heterogeneous GNN for Relational Deep Learning
Authors: Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger
Journal: Transactions on Machine Learning Research (TMLR)
Status: Accepted
Year: 2025
A Simple and Expressive Graph Neural Network-Based Method for Structural Link Representation
Authors: Veronica Lachi, Francesco Ferrini, Antonio Longa, Bruno Lepri, Andrea Passerini
Conference: Proceedings of Machine Learning Research (PMLR)
Year: 2024
Meta-Path Learning for Multi-Relational Graph Neural Networks
Authors: Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger
Conference: Learning on Graphs Conference (LoG)
Accepted as: Oral
Year: 2023
Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
Authors: Wamiq Raza, Anas Osman, Francesco Ferrini, Francesco De Natale
Journal: Drones (MDPI)
Year: 2021