Research Interests

• statistical learning
• decision-making
• game theory, particularly learning in games
• reinforcement learning
• stochastic approximation

External Roles

I am a member of the Scientific Board of the Smith Institute.

I am currently an external examiner for Edinburgh University and Imperial College London. Previously I was an external examiner at the University of Kent at Canterbury and the University of Leicester.

I have held numerous roles at the Royal Statistical Society. These include elected Council member, member of the long term strategy group, member of the Academic Affairs Advisory Group, and chair of the Applied Probability Section, and member of the Research Section Committee.

I have served on the programme committee of the London Mathematical Society.

I was a member of the Review into Knowledge Exchange in the Mathematical Sciences (aka the Bond Review) and the subsequent Council for the Mathematical Sciences exploration into the establishment of an Academy of Mathematical Sciences.

PhD Supervisions Completed

I have had the privilege to supervise some great students. The following have finished and moved on:

• Archie Chapman
• Michalis Smyrnakis
• Harriet Mills
• Ben May
• Steve Perkins
• Kevin Lloyd
• Matthew Arnold
• Simon Smith
• Adnane Ez-zizi
• Beki Floyd
• Hannah Julienne
• James Edwards
• Chris Sherfield
• Simon Smith
• Ciara Pike-Burke
• James Grant
• Henry Moss
• Tom Pinder
• Anja Stein

I am always on the lookout for the next good research student. However, note that it is essentially impossible for me to find financial support for students from outside the EU.

PhD Supervision Interests

Bandits in real systems Multi-armed bandit theory [https://en.wikipedia.org/wiki/Multi-armed_bandit] is extremely well-studied in situations where there is a very direct link between actions and rewards. However in many situations where we may wish to deploy these techniques, the choice of an action leads to outcomes in a complex and partially-understood way. For example, choosing the price of a finitely-available product for the following day will result in a semi-predictable sales pattern, and consequent amount of stock left at the end of the day. And choosing some hyper-parameters of a learning method for a period of time will result in a semi-predictable performance improvement of the method. This project will develop techniques for such problems, where there is a (semi-)parameterised model of the world, and sequential decisions must be taken to simultaneously learn the model and optimise outcomes.