Interests: Statistics, renewable energy, machine learning, optimisation.

PhD research

Supervisors: Axel Gandy (Imperial College London), 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 (first author)

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

Academic papers (contributing author)

PhD thesis