My main area of interest lies in machine learning, deep learning and artificial intelligence and their applications in agriculture, specifically, data-driven phenotype prediction in dairy cattle. I have been working on predictions of economically important traits for over 5 years. Additionally, I have a keen interest in programming and software development, computer vision, quantitative genetics, and genomics. I still maintain an interest in the field of mathematics and my previous research area of mathematical biology and epidemiology.

I currently teach on the Applied Animal Science undergraduate degree programme within the Department of Animal Veterinary Science. Specifically, I am module lead for Data Science and Computing (P110-1Z). This 1st year module aims to provide students with the underlying skills and knowledge required to handle and analyse data. Teaching centres on the following core areas: use of a range of computer software suitable for a multitude of tasks; processing data to provide coherent structure; producing graphical and numerical summaries of data; identifying a range of basic statistical and computing concepts.

I currently supervise students at both undergraduate and postgraduate level providing honours and masters projects. Projects are generally centred around phenotype prediction, machine learning, genetics, and big data.