Charles Rahal
Senior Departmental Research Lecturer
I am a senior departmental research lecturer in computational social science at the University of Oxford, where I work with colleagues at the Demographic Science Unit and Leverhulme Centre for Demographic Science (where I form part of the Senior Management Board). I am a co-investigator at the ESRC funded Centre for Care (and on another ESRC Strategic Research Grant), and act as the local network lead co-ordinator for the UK Reproducibility Network (as part of the steering of Reproducible Research Oxford). I was previously a British Academy Postdoctoral Fellow. My training includes degrees, diplomas, and certificates in computational econometrics, economics, advanced research methods, and investment and finance.
My research focuses on methodological innovations which uncover patterns in large-scale observational data with a focus on equality and equity. It is usually motivated by a desire to improve policies and public administration. This most recently includes but is not limited to population-wide scientometric analysis, model evaluation in machine learning, and computational approaches to the life course (broadly defined). I have recently been involved in several successful funding applications (totalling around £12m) and have published in many of the world's leading journals. I predominantly work in Python, Bash and TeX, and take great pride in being able to generate policy impact - having won awards and commendations for contributions to the UK government Covid-19 policy response - all through open and reproducible research.
Recent publications
-
The Rise of Machine Learning in the Academic Social Sciences
Journal article
Rahal C. et al, (2022), AI & Society
-
Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries.
Journal article
Aburto JM. et al, (2022), Int J Epidemiol, 51, 63 - 74
-
Population Studies at 75 years: An empirical review.
Journal article
Mills MC. and Rahal C., (2021), Popul Stud (Camb), 75, 7 - 25
-
Quantifying impacts of the COVID-19 pandemic through life expectancy losses: a population-level study of 29 countries
Journal article
Aburto JM. et al, (2021)
-
Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world.
Journal article
Block P. et al, (2020), Nat Hum Behav, 4, 588 - 596