Machine learning leader with 10+ years’ experience spanning applied research and delivery in machine learning, autonomous systems, and robotics for defence and national security. I set technical direction, lead teams, and turn ambiguous operational problems into deployed ML solutions.
I’m a Lead Machine Learning Scientist at Mind Foundry, with 10+ years’ experience spanning applied research and delivery leadership in machine learning, autonomous systems, robotics in defence and national security. I lead cross-functional teams of ML scientists and engineers, line manage senior technical staff, and work closely with customers and commercial stakeholders to turn ambiguous operational problems into deployed ML solutions — spanning computer vision, anomaly detection, predictive maintenance, electronic surveillance, and localisation and tracking.
Before moving into industry, I spent seven years as a researcher at the University of Bristol working on multi-agent systems, decision-making, and autonomous vehicles, including the UK’s flagship driverless car project (Venturer) and a PhD in Aerospace Engineering focused on optimal routing for commercial formation flight.
You can find my academic publications below, or see my Google Scholar and ResearchGate profiles for the full record.
Technical areas I work across:Thesis: “Optimal Routing and Assignment for Commercial Formation Flight”.
Developed a scalable analytic-geometric approach to commercial formation flight routing, enabling fast cost estimation and large-scale fleet assignment via Mixed Integer Linear Programming. Demonstrated fuel savings of 8-13% in a Transatlantic case study, with consistent gains shown across flights of varied distance, geography, and formation size.