Finding structures in astronomical data with machine learning
Astronomy is experiencing a rapid growth in complexity and data size. The difficulty in analysing data fosters the development of data-driven science as a useful companion where astronomers develop tools to mine datasets and extract novel information from them. In recent years, machine learning algorithms have been popular risen among astronomers, and are now used for a wide variety of tasks.
Metric diophantine approximation on non-degenerate manifold
Detecting high-frequency modes in solar-like stars
To search for pseudo-modes in solar-like stars and search for pattern in pseudo-mode frequencies as a function of time.
"Liverpool, Hands Off Wales": Memory in North Wales
On The Bounce: Mathematical Modelling of a Bouncing Fluid Droplet
The use of the Red Mason bee, Osmia Bicornis, as a managed pollinator of orchard crops
“I am the first of the camp, the last of the legion: A study of Roman military standards and their significance to the average legionary”