Reinforcement Learning in Closed-loop data science

This Ph.D. project will explore the use of reinforcement learning and causal modelling in data science applications which involve closed-loop scenarios for either the data acquisition or the application of the final trained controller.
Aims and objectives.
This is part of the EPSRC New Approaches to Data Science Programme, specifically the project Closed-Loop Data Science. This project is about understanding the impact of unmodelled feedback loops on data science practice, and the incorporation of methods from Control Engineering into Data Science. Potential applications will come from computational biology, travel, music recommender systems, smart cities and finance.
Novelty of the research methodology
This project is about understanding the impact of unmodelled feedback loops on data science practice, which has been an important issue often ignored in the past. It is also novel as it includes the incorporation of methods from Control Engineering into Data Science.

Alignment to Research Council's strategies and research areas, and Collaborators
This is part of the EPSRC New Approaches to Data Science Programme, specifically the project Closed-Loop Data Science.
Potential applications will come from computational biology, travel, music recommender systems, smart cities and finance, so collaborators include:
Glasgow Polyomics, JP Morgan, Skyscanner, Moodagent, Aegean airlines.