Mark is Professor of Statistical Epidemiology in the Leeds Institute for Data Analytics (LIDA), University of Leeds, and a Fellow of the Alan Turing Institute for Data Science and Artificial Intelligence.
Trained as a mathematical physicist, Mark's driving interest centres on improving our understanding of the observable world through modelling. After his PhD, Mark worked as a consultant data analyst before entering academia and has since fashioned a programme of interdisciplinary research that spans the gap between theoretical and applied data analytics. He focuses on modelling complexity and highlighting and solving common analytical problems in observational research.
Mark's research and teaching interests have converged around the insights and utility of causal inference methods, and how these might be integrated with machine learning and AI; he is also a recognised expert in latent variable modelling and the analysis of longitudinal data.
Mark is especially keen on further developing and promoting the unique collaborative and interdisciplinary opportunities provided by LIDA and his Turing Fellowship; he is actively engaged across the Turing network in promoting the development and application of causal inference methods.
Mark is seeking to understand complex relationships between individuals within their natural environment through the development and application of observational methods, specifically through the integration of causal inference modelling and agent-based modelling. An example domain of this challenge is modelling patterns, causes and consequences of obesity within our society – more recently, these approaches were also applied to Covid-19 pandemic.
Mark is also interested in ‘algorithmic explainability’ and the development of ‘ethical’ and ‘smart’ AI, i.e. the use of causal inference methodology to understand the operations and consequences of machine learning and artificial intelligence, to ensure that we always pursue fairness in the deployment and impact of these technologies within society.