I hold a Bachelor’s degree in Electronics and Electrical Engineering, followed by an MSc in Robotics Engineering (Distinction) from the University of Bristol. After completing my Postgraduate studies, I spent 1.5 years working as a Robotics Software Engineer at an agricultural robotics company in Cambridge, where I focused on transforming theoretical and mathematical concepts into practical software solutions to enhance robotic intelligence and performance.

I am currently a PhD Student in Sensing, Processing, and AI for Defence and Security (SPADS CDT), jointly hosted by the University of Edinburgh and Heriot-Watt University. My research explores the development of spiking neural networks (SNNs) using Bayesian deep learning, with the aim of creating neuromorphic systems that are both computationally efficient and mathematically robust. By integrating probabilistic reasoning with biologically inspired neural computation, I hope to advance the theoretical underpinnings of modern SNNs and improve their practical efficiency for future intelligent systems.

My broader research interests include machine learning, probabilistic modelling, and computational neuroscience, with a strong motivation to connect innovative mathematical theory with real-world application in AI, intelligent systems and robotics.