Michael was raised in Middlesbrough, UK, before pursuing his undergraduate degree in Mathematics at the University of Cambridge, where he focused on analysis and algebra. He developed a strong interest in geometry and number theory while completing his first master’s degree in Mathematics at King’s College London, producing a dissertation on additive number theory and Waring-like problems. After spending some time working as a software engineer for an AI cybersecurity company, Michael earned a second master’s degree in Machine Learning from UCL. His projects spanned reinforcement learning, Bayesian neural networks, and statistical learning theory, culminating in a dissertation on adaptive kernel methods in causal inference.
During the programme, Michael will collaborate with his advisors to explore sequential decision-making (bandit algorithms) with applications in healthcare, clinical trials, and recommender systems. His broader research interests include kernel methods and Bayesian deep learning. In his free time, Michael enjoys cooking, swimming, rock climbing, and discovering new spots around London
