Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Lecturer in Statistics



+44 (0)20 7594 2976c.pike-burke




522Huxley BuildingSouth Kensington Campus





I am a Lecturer in Statistics in the Department of Mathematics at Imperial College London.

My research is in the field of statistical machine learning. I am particularly interested in sequential decision making problems, where the goal is to learn to make optimal decisions by sequentially interacting with an unknown environment. Some examples of problems I have worked on include variants of the multi-armed bandit, online learning, and reinforcement learning problems.

For further details of my research please see my personal website.



Johnson E, Pike-Burke C, Rebeschini P, Optimal convergence rate for exact policy mirror descent in discounted Markov decision processes, Neural Information Processing Systems (NeurIPS 2023)

Robert A, Pike-Burke C, Faisal A, Sample complexity of goal-conditioned hierarchical reinforcement learning, Neural Information Processing Systems (NeurIPS 2023)

Vakili S, Ahmed D, Bernacchia A, et al., 2023, Delayed feedback in kernel bandits, 40th International Conference on Machine Learning, ML Research Press, Pages:34779-34792, ISSN:2640-3498

van der Hoeven D, Pike-Burke C, Qiu H, et al., 2023, Trading-off payments and accuracy in online classification with paid stochastic experts, 40th International Conference on Machine Learning, ML Research Press, Pages:34809-34830, ISSN:2640-3498

Howson B, Pike-Burke C, Filippi S, 2023, Delayed feedback in generalised linear bandits revisited, Artificial Intelligence and Statistics s (AISTATS 2023), PMLR, Pages:1-25, ISSN:2640-3498

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