I am a computer scientist working as a Postdoctoral Research Fellow in the Department of Computer Science at University College London together with Mirco Musolesi as a part of the Machine Intelligence lab. I create and study artificial intelligence techniques for networked systems, with a particular interest in developing algorithms for challenging decision-making problems that arise in the real world.

My toolkit includes techniques from network science, reinforcement learning and planning, deep learning (including graph neural networks), game theory, and multi-agent systems. I am broadly interested in both fundamental research and applications.


[May 2024] New pre-print: Large Language Models are Effective Priors for Causal Graph Discovery. This work studies the potential of leveraging LLMs as sources of prior information in the causal discovery process, highlighting their potential as well as shortcomings.

[May 2024] I will be joining the Goal-Oriented Long-Lived Systems (GOALS) group at the Oxford Robotics Institute as a Postdoctoral Researcher from August 2024. I am beyond thrilled for this new adventure!

[May 2024] Our work Trust-based Consensus in Multi-Agent Reinforcement Learning Systems has been accepted for presentation at the First Reinforcement Learning Conference (RLC). In this paper, we propose a trust mechanism for dealing with unreliable actors in decentralized multi-agent systems, and empirically show its effectiveness for solving a set of consensus environments.

[Apr 2024] A new preprint is now on arXiv, Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective. In this survey, we review works that have approached optimization problems over graphs with reinforcement learning techniques. Specifically, we focus on problems that currently do not have satisfactory exact or heuristic solutions, and for which RL can be advantageous.