Matteo Tiezzi

Matteo Tiezzi is a post-doctoral researcher in the PAVIS (Pattern Analysis and Computer Vision) group at the Italian Institute of Technology (IIT), working within SAILAB, a research laboratory affiliated with the University of Siena. His work focuses on advancing machine learning models for dynamic and continual learning scenarios.

Research Interests: His primary research lies in Lifelong Learning and Graph Representation Learning, with a focus on developing algorithms that enable models to learn continuously from non-stationary data streams, particularly in visual domains. He explores foundational learning laws inspired by the principle of Least Cognitive Action and investigates motion and attention-based invariances for robust feature learning.

Publication Trends: His recent publications reflect a strong trajectory in continual learning and graph neural networks, with papers accepted at AAAI, IJCNN, and CoLLAs. These works emphasize theoretical innovation—such as Lagrangian frameworks and Riccati equations—applied to deep learning architectures, particularly for pre-trained models and dynamic environments.

Scientific Awards:

• 1st place Hackathon Soccer Data Challenge – Recognized for using machine learning to analyze football players' roles and team strategies.

Advising and Grants: While no formal students or grants are listed in the provided text, Matteo has played a significant role in software development and open-source dissemination, contributing to the research community through tools like LP-GNN and SAILenv. His PhD work and postdoctoral research suggest active involvement in collaborative, grant-funded projects within SAILAB and IIT.

Labs and Teams: He is a core member of the SAILAB at the University of Siena and currently contributes to the PAVIS group at the Italian Institute of Technology. SAILAB focuses on intelligent agents in visual environments, lifelong learning, and graph-based learning systems. He has contributed to the development of SAILenv, a Unity3D-based virtual environment with pixel-level annotations for training and testing visual learning agents.