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.

News

[Mar 2024] Our paper PRORL: Proactive Resource Orchestrator for Open RANs Using Deep Reinforcement Learning has been published in IEEE Transactions on Network and Service Management. We propose a reinforcement learning approach for dynamic allocation and orchestration of resources for the O-RAN infrastructure that underlies 5G communication technology.

[Nov 2023] New work: Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery. We address the problem of discovering causal graphs with a model-based reinforcement learning method, which is powered by an incremental algorithm for determining cycle-inducing edges, and is shown to compare favorably to model-free RL methods and greedy search.

[Apr 2023] Thrilled to have passed my PhD viva with no corrections for my dissertation "Learning to Optimise Networked Systems". I am grateful to my examiners Pietro Liò (University of Cambridge) and Simon Julier (UCL) for the stimulating conversation.