Departmental Research Lecturer
Charles is a social science methodologist and applied social data scientist with a background in high-dimensional econometrics, having completed his PhD in 2016. He is a Departmental Research Lecturer at the Leverhulme Centre for Demographic Science, a Co-Investigator at the ESRC Centre for Care, a Steering Group member of Reproducible Research Oxford, and an Associate Member of Nuffield College. As part of his lecturing, he co-convenes Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He is always interested in hearing from potential co-authors or prospective graduate students who share his enthusiasm for using Python, LaTeX, and other open-source tools for academic research.
Charles is particularly interested in unique Big Data origination processes, be they unstructured or otherwise, and has a specific interest in model uncertainty. Other current areas of interest include civic technology, applied econometrics (predominantly spatial and time series), scientometrics, public health, historical demography, and social inequality, mobility and stratification more generally.
Specific projects underway at present include:
- A large scientometric review of the 'Evolution of Science';
- Work on inequalities in life expectancy and health across the very long run (the 'Legacy of Longevity');
- The culmination of his BA PDF which takes a granular approach to procurement across the NHS;
- An efficient library to incorporate model selection, averaging, and other types of robustness;
- Various projects which analyse the distribution of corporate control and 'elites' more generally.
Follow the development of his projects on GitHub, his published academic writings on Google Scholar, and find more information on his academic homepage.
Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries
Aburto JM. et al, (2022), International Journal of Epidemiology, 51, 63 - 74
The Rise of Machine Learning in the Academic Social Sciences
Rahal C. et al, (2021)