I am a Doctoral Researcher at the University of Edinburgh developing the statistical foundational framework for advanced Bayesian graphical modelling aimed at real-time uncertainty quantification and evidence fusion in complex-contested-multi-domain environments. The work builds on Bayesian graphical and inferential methods for fusing heterogeneous information across distributed autonomous systems, with a particular focus on inferring latent causal variables (i.e., operationally meaningful states that are never directly observed but can be reconstructed from indirect, noisy, and adversarially perturbed signals) and on coordinating swarm intelligence at the tactical edge, where agents must reason and act locally under intermittent connectivity. The framework will deploy machine learning based emulators within dynamic-data-driven systems.
The research is funded by the UK's Ministry of Defence (MoD) and the motivation is around the applications of modular digital twin architectures for autonomous systems (spanning aerial, underwater, and ground-based robotics) operating in defence and security contexts, where interpretable and high-fidelity predictive analysis is critical. This work is carried out in close collaboration with the UK's Defence Science and Technology Laboratory (DSTL) and the Royal Air Force's Rapid Capabilities Office.
I studied Engineering (bachelor's and master's) at the University of Cambridge, specialising in Transformational Machine Learning. I previously interned at the Zuse Institute Berlin, where I worked on research in explainable AI. I am also co-authoring a forthcoming book with a senior lecturer in Hindu Studies at the Cambridge Faculty of Divinity.
I have held a Senior Modelling Engineer remit at Network Rail, where I have been closely involved in shaping R&D initiatives that integrate ML and advanced analytical methods for infrastructure fault prediction. Through this work, I have engaged extensively with specialists from organisations including Deloitte, Cognizant, Capgemini, Faculty AI, and Arthur D. Little.