My research focuses on Ant Colony Optimization, autonomous navigation, and agent-based models of collective trust and decision-making.
I am a Research Fellow at the Department of Mathematics and Computer Science, University of Catania, and a member of the Complex Intelligent Systems (CIS) Research Group. I completed my Master's degree in Physics in 2020, with a focus on Complex Systems Physics, and my PhD in Computer Science in 2024, both at the University of Catania. My PhD thesis, "From Ants to Crowds: Harnessing Competition for Optimization," is available online, and my full resume can be found here.
Alongside my research, I am actively involved in the optimization and metaheuristics community. I served as Chair of the International Workshop on Big Optimization (WBO 2025), and I currently serve as Guest Editor for two Springer collections: Advances in Large-Scale and Intelligent Optimization: Algorithms, Models, and Applications, published in Soft Computing, and Bio-Inspired Adaptive Systems for Collective Behaviors and Intelligent Optimization, published in Discover Computing. I also serve as a reviewer for several journals and conferences, including Applied Soft Computing, Artificial Life, MIC, and ECAI.
I study how competition, trust, and exploration shape intelligent behavior in algorithms and multi-agent systems, from optimization to autonomous navigation in unknown environments.
My research investigates agent-based extensions of Ant Colony Optimization, examining how introducing controlled competition among ants disrupts herd effects, fosters exploration, and improves performance in complex optimization environments. This line of work forms the core of my PhD thesis and continues to shape my broader interest in cooperative and competitive dynamics within collective systems.
A parallel strand of my research reinterprets the internal mechanics of ACO through a social lens: pheromone trails and local visibility become measures of social and objective trust, while their relative weights capture how strongly an agent relies on each. This mapping, which I call ACO Mapping, offers a compact framework for studying collective decision-making and trust formation in multi-agent systems. See a simple demo here.
I also study how autonomous agents explore unknown, maze-like environments modelled as grid worlds with walls, traversal costs, and charging stations. Agents must continuously balance exploration, energy consumption, and the decision to recharge, and I compare how different navigation strategies and individual traits affect their ability to survive and complete the mission.
Recent years are shown by default. Toggle to view older years.
Collaboration with Prof. Angelo Cangelosi (University of Manchester) and Dr. Marta Romeo (Heriot-Watt University) — human–robot interaction & trust in cooperative tasks.
Courses, guest lectures, and materials for students.
| Course | Term | Degree Program | Materials |
|---|---|---|---|
| Artificial Intelligence (3 ECTS – 24h) | AY 2025/2026 | Bachelor's in Computer Science (L-31) | Slides |
| Principles of Computer Science (2 ECTS – 19h) | AY 2025/2026 | Bachelor's in Biotechnology (L2) | - |
| Principles of Computer Science (2 ECTS – 19h) | AY 2024/2025 | Bachelor's in Biotechnology (L2) | Materials |
I have supervised and co-supervised several students theses
In my free time, I like to play with generative AI to create small interactive experiences and games.
Desktop maze navigation game with procedurally generated levels. Collect Orbs to unlock hints that reveal the path to the exit.
Discover how to find the shortest route that visits all cities exactly once and returns to the starting point. Experiment with different algorithms and understand how optimization techniques work.
Assign colors to nodes in a network and solve conflicts between neighboring nodes. Learn how graph coloring is used in real-world problems like scheduling and resource allocation.
Uncover hidden patterns behind customer spending and frequency data. Discover how clustering techniques can be used to segment and analyze groups of clients.
A small interactive model to understand collective behaviors and multi-agent systems. Explore how simple rules can generate complex dynamics, from ant colonies to human crowds.
Science communication under the motto "Ludendo Docere" (teaching through play): using games to introduce and explain key concepts in optimization research.
Photos from events and activities.
