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 network methodologies for combining heterogeneous information and deploys 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 have previously conducted research in explainable AI at the Zuse Institute Berlin and studied Engineering (bachelor's and master's) at the University of Cambridge, specialising in Transformational Machine Learning. I am 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.