About
University of Oxford / Data Science
I’m an Associate Professor in Data Science and Informatics at the University of Oxford.
My research brings together model uncertainty, applied data science, and the study of science itself. I build open tools for working with complex data, including the GWAS Diversity Monitor and RobustiPy, and am active in the Open Science movement.
Primary Interests
Model uncertainty
Methodology, machine learning, and deep learning for health and social science.
Data in the wild
Applied data science, working with unstructured data found in the real world.
Science at scale
Scientometrics and data analysis of the scientific record at scale.
I convene the Metrics and Models lab, which has an open seminar series; all are welcome to attend.
Background
I’m a former British Academy Postdoctoral Fellow at Oxford and an Honorary Visiting Researcher at Peking University. I am also an Associate Member at Nuffield College and a Researcher at the Gradel Institute, New College. I was a Sino-British Visiting Fellow at the University of Hong Kong from January-March 2026.
I am a Co-Investigator or Principal Investigator on various projects across the University. I’ve consulted on a Methods Advisory Group for the ONS (pro bono), as well as for Banco de la República. We’ve also recently started a data science consultancy, Patterns and Proofs; please contact charlie@patterns-and-proofs.com with enquiries.
Editorial & community roles
I am an Associate Editor-In-Chief at the Journal of Social Computing, serve in a senior TPC role at the International Conference on Social Computing as committee chair of the 7th edition, and am an Associate Editor at ACM Transactions on Social Computing and Transactions on Graph Intelligence and Network Applications.
I’m currently a moderator of SocArXiv and a Research Affiliate at the Public Knowledge Project. Find me on Bluesky.
Research
Browse the peer reviewed publications for papers, code, and citations, or explore selected works in progress.
Journals & publication venues
With many thanks to some truly excellent co-authors, my work has recently been published in: Patterns, Multivariate Behavioral Research, Nature Computational Science, PLOS ONE, Journal of Computational Social Science, Journal of the Royal Statistical Society: Series A, Psychometrika, Nature Climate Change, AI & Society, Population Studies, the International Journal of Epidemiology, Nature Genetics, the new Nature sub-journal Communications Biology, the American Sociological Review, Nature Human Behaviour, the International Journal of Population Data Science, Kyklos, the Journal of Housing Economics and Computational Economics. Find the code for those papers on GitHub here.
Teaching
I currently lead on teaching in Population Data Science in various forms. I have previously taught Demographic Analysis, Life Course Research, Replication and Transparency in Open Social Science (a replication project and accompanying discussion group), Python for Sociologists, Econometrics with Financial Applications, and An Introduction to Econometric Software. I also give guest lectures and workshops on ‘An Introduction to the Command Line’, ‘Machine Learning for Social Scientists’, the LaTeX typesetting language, and help to co-convene various summer and autumn schools (e.g. SICSS-Oxford, OxBer, CfC). We are in the process of setting up a series of short courses related to Population Data Science here at the Leverhulme Centre for Demographic Science; please see here for more information.
Prospective Students
I currently supervise methodologically advanced students in various areas of computational science. I am increasingly looking to supervise students in the area of deep learning applications for health and social science with a focus on large-scale population registers. I am also interested in large-scale bibliometric research. See here and here for details of two projects that we had open in 2025/2026; we will shortly be advertising new DPhil projects for the 2026/2027 application cycle. Please get in contact with regards to pre- or post-doctoral fellowship opportunities which we may have.
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