IDSAI Research Fellows

The Institute for Data Science and Artificial Intelligence has appointed four Research Fellows since July 2019. The research fellows have split roles within the Institute; they conduct their own independent research alongside undertaking pilot research projects aligned to the aims of the IDSAI and the Alan Turing Institute.

Finley Gibson

Finley joined IDSAI in September 2022 following his PhD with the University of Exeter. With a background in physics and robotics, Finley's research interests centre on machine learning and optimisation. In particular optimisation of expensive multi-objective problems, and applications of optimisation to the tuning of machine learning algorithms.

Data science expertise: Bayesian optimisation, deep neural networks, multi-objective optimisation, image classification and automated hyperparameter tuning.

Charlie Kirkwood

Charlie joined the Institute in October 2022 following his PhD in mathematics at the University of Exeter in partnership with the Met Office. Charlie’s background was originally in geology, and he has spent the last 7+ years learning how data science and artificial intelligence can help us to model and understand the environment; a topic in which he has a range of publications. Charlie is passionate about inclusivity and empowering scientists to make use of, and contribute to, emerging technologies.

Data science interests: Neural networks, gaussian processes, deep learning, Bayesian statistics, ensemble methods, decision trees, model checking & calibration, interpretable AI, visualisation.

Emily Price

Emily joined IDSAI in August 2022 following her PhD with the University of Exeter. With a background in behavioural science, Emily’s research interests centre on understanding the behaviour of animals and humans at the individual level using large datasets. In particular, optimising the classification of bio-logging data using supervised and unsupervised machine learning algorithms.

Data science expertise: Supervised and unsupervised machine learning, network theory and time series.