Professor Mark S Gilthorpe
- Position: Professor of Statistical Epidemiology
- Areas of expertise: Observational data science; causal inference; health data analytics; lifecourse methods; latent variable modelling
- Email: M.S.Gilthorpe@leeds.ac.uk
- Phone: +44(0)113 343 1913
- Location: Room 11.21, Leeds Institute for Data Analytics Worsley Building
- Website: ORCID
Profile
Mark is Professor of Statistical Epidemiology in the School of Medicine and the Leeds Institute for Data Analytics (LIDA), 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, he spent time as a consultant data analyst before being recruited into academia. Mark has since fashioned a programme of interdisciplinary research that spans the gap between theoretical and applied data analytics, focussing particularly 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. As Deputy Director for LIDA (Research and Innovation), Mark is especially keen on further developing and promoting the unique collaborative and cross-disciplinary opportunities provided by LIDA. As a Turing Fellow, Mark is actively engaging across the Turing network to promote the development and application of causal inference methods.
Responsibilities
- Deputy Director (Research & Innovation) of LIDA (https://lida.leeds.ac.uk/)
Research interests
Observational data analysis and causal inference.
Qualifications
- BSc
- PhD
Professional memberships
- FHEA
- Alan Turing Fellow
Student education
Module lead and educator on the MSc in Health Data Analytics for "EPIB5042M Modelling Prediction & Causality for Observational Data" and "EPIB5046M Latent Variable Methods"; module co-lead and co-educator for "EPIB5045M Modelling Strategies for Causal Inference in Observational Data". Programme co-lead and educator on the Summer School "Causal inference with observational data: challenges and pitfalls".
Research groups and institutes
- Clinical and Population Science
- Leeds Institute of Cardiovascular and Metabolic Medicine