I am an early-stage Doctoral Researcher at the University of Edinburgh, focused on developing next-generation modular digital twin frameworks using Bayesian networks for real-time uncertainty quantification and predictive analysis in autonomous systems (aerial drones, underwater autonomous vehicles, and ground-based robotics) operating in complex-contested-multi-domain environments. ML based surrogate models will be employed to approximate complex system dynamics and enable faster simulations within the digital twin architecture.
I have previously conducted research in explainable AI at the Zuse Institute Berlin and studied Chemical Engineering at the University of Cambridge, specialising in Transformational Machine Learning. I maintain an ongoing collaborative research with the Faculty of Divinity at Cambridge.
I also serve as a Senior Modelling Engineer at Network Rail, where I co-lead R&D initiatives integrating machine learning and advanced analytics for infrastructure fault prediction. In this capacity, I have engaged with experts from leading firms including Deloitte, Cognizant, Capgemini, Faculty AI and Arthur D. Little.