Research interests: Computational statistics, renewable energy, machine/deep learning.

PhD research

Supervisors: Axel Gandy (Imperial College London, lead) and David Brayshaw (University of Reading).

My PhD focuses on using statistics to make the best decisions in the energy transition, particularly in the electricity sector. Such decisions (e.g. whether to build a wind farm, battery or new transmission line) are typically difficult due to the complexity of electricity grids and considerable uncertainty regarding future government policy, grid developments, electricity demand/price patterns and weather events.

For this reason, electricity strategy is typically informed by large amounts of data. For example, to determine the best location of a new wind farm, historic wind speeds and electricity prices may be employed, along with a model of the grid. This leads to a natural statistics problem: how do we use the available data to make the best decisions in the energy transition?


See Google Scholar page for up-to-date publication list.

Academic papers

1Runner-up for Roy Billinton Award for best student paper
2See 2020 importance subsampling paper for generalised version




I helped produce a Coursera course in Tensorflow 2, which came out in 2020. As of October 2020, over 10,000 people have taken the course and it has a rating of 4.9/5. It’s free to take for anyone! Check it out here.

Teaching assistant

Year Description
2019 Machine Learning (university level summer school)
2019 Machine Learning (master level data science for healthcare course)
2019 Mathematical Methods (first year undergraduate mathematics course)
2018 Data & Uncertainty (master’s level mathematics course)

All courses at Imperial College London.