Research Fellows
The Research Fellows develop new interdisciplinary collaborations through the Centre and contribute to its mission of advancing creative, impactful and responsible data science and AI research.
Dr Karen Jeffrey
Dr Karen Jeffrey's path into data science developed through her work in consultancy and at an economic think tank, where she saw how powerful the influence of data-driven evidence in shaping policy decisions could be. During her PhD in Political Economy, she taught herself to code and began using advanced statistical and experimental methods to investigate how economic forces shape political behaviour.
More recently, she worked in medical informatics analysing large-scale Scottish health records to study long COVID. Her current research focuses on how artificial intelligence is reshaping the economy and how workers and voters respond to these changes. By combining political economy methods with data science approaches, Dr Jeffrey aims to produce research that informs policy and helps ensure the benefits of AI are shared widely.
Dr Catherine Higham
Dr Catherine Higham brings a diverse background spanning mathematics, bioinformatics, and artificial intelligence.
After completing a mathematics degree at the University of Oxford, she worked in the private sector analysing consumer marketing strategies before returning to academia through a Daphne Jackson Fellowship at the University of Glasgow. This was followed by a Lord Kelvin/Adam Smith PhD studentship focused on studying Somatic DNA mutations.
Her current research sits at the intersection of AI, machine learning, and emerging quantum technologies. Dr Higham’s work explores generative models that simulate complex data, hybrid classical–quantum algorithms designed to work with today’s quantum hardware limitations, and active inference methods that help AI systems make decisions under uncertainty. Through collaborations with experimental physicists, she has applied computational approaches to cutting-edge technologies including single pixel cameras, LiDAR systems, and quantum annealers.
Dr James Tanner
Dr James Tanner's research background combines approaches from linguistics, speech science, and quantitative modelling. Combining his research on speech variability and his experience in developing commercial speech synthesis technology, James's research in collaboration with colleagues from the UK, US, Canada, and Japan is focused on how linguistic information is represented within AI speech models and characterising how AI-generated 'deepfake' speech compares with human speech.
Dr Lawrence Bull
Dr Lawrence Bull is researching machine learning for physical systems, particularly tools to encode structure (via domain knowledge) into data-driven models - mostly condition/health monitoring of machines and infrastructure, utilising telemetry data. Applications have included planes, trains, automobiles, and wind turbines.
By encoding domain expertise into statistical models, complex representations can be built from interpretable units, increasing the value of sensing and fusing physics with data.