I am a Research Fellow in Artificial Intelligence at the University of Southampton, having recently completed my Ph.D. in emergent communication. I’m currently applying machine learning techniques to maritime engineering challenges, working with industry partners on optimizing car carrier designs through neural networks that estimate wave-induced power requirements. My doctoral research investigated how autonomous agents develop language from scratch. I focused on temporal dynamics in agent communication, designing architectures that enable temporal referencing and improve strategy development. My research examining spatio-temporal relationships in interpretable emergent communication has been published in venues such as NeurIPS and ICLR.
PhD in Artificial Intelligence, 2020–2025
University of Southampton
BSc in Computer Science, 2016–2020
University of Liverpool
Effective communication requires the ability to refer to specific parts of an observation in relation to others. While emergent communication literature shows success in developing various language properties, no research has shown the emergence of such positional references. This paper demonstrates how agents can communicate about spatial relationships within their observations. The results indicate that agents can develop a language capable of expressing the relationships between parts of their observation, achieving over 90% accuracy when trained in a referential game which requires such communication. Using a collocation measure, we demonstrate how the agents create such references. This analysis suggests that agents use a mixture of non-compositional and compositional messages to convey spatial relationships. We also show that the emergent language is interpretable by humans. The translation accuracy is tested by communicating with the receiver agent, where the receiver achieves over 78% accuracy using parts of this lexicon, confirming that the interpretation of the emergent language was successful.