Progress in sensing, computational power, storage and analytic tools has given us access to enormous amounts of complex data, which can inform us of better ways to manage our cities, run our companies or develop new medicines. However, the ’elephant in the room’ is that when we act on that data we change the world, potentially invalidating the older data. Similarly, when monitoring living cities or companies, we are not able to run clean experiments on them - we get data which is affected by the way they are run today, which limits our ability to model these complex systems. We need ways to run ongoing experiments on such complex systems. We also need to support human interactions with large and complex data sets. In this project we will look at the overlap between the challenge someone faces when coping with all the choices associated with booking a flight for a weekend away, and an expert running complex experiments in a laboratory.
Reinforcement learning is a key tool in adapting closed-loop system performance. The student will have the opportunity to apply techniques together with applications problems from our research collaborators, which include Amazon, Moodagent, Skyscanner, JP Morgan and Widex.
Funding is available to cover tuition fees for UK/EU applicants for 3.5 years, as well as paying a stipend at the Research Council rate (estimated £14,999 for Session 2019-20).