How can machine learning and optimisation be applied not just to understand trade and logistics systems, but to actively improve them: removing friction, predicting failure and making supply chains smarter?

My research sits at the intersection of machine learning, combinatorial optimisation and operational intelligence, applied to trade and logistics, where inefficiency is costly and data is underused. My BSc thesis on traffic signal optimisation was a first proof of concept, and my MSc dissertation applies surrogate modelling and optimisation to manufacturing process parameters, balancing quality, energy use and cost. Future directions include hybrid reinforcement learning and metaheuristic solvers for freight routing, early warning systems for supply chains, and data integration for trade operations.