Leo grew up in Durham before moving to London for his BSc in Mathematics at Imperial College. There he grew interested in the intersection of geometry and machine learning from modules in statistics and algebraic topology, etc. He further developed this interest through his subsequent MSc in Statistical Science at the University of Oxford, writing his master’s thesis on applying variational inference to scale up Bayesian nonparametric approaches to manifold learning and analysing their convergence rates. On the StatML program, he intends to continue working on the geometric side of machine learning, such as deep generative modelling for manifold-valued data, as well as pursuing other interests in data efficient learning – in meta-learning, Bayesian learning etc.
Leo Zhang
