Dr Unwin's PhD was in uncertainty quantification in engineering systems using the Multilevel Monte Carlo method at the University of Cambridge. Then she switched fields to modelling infectious disease transmission in the School of Public Health at Imperial College London, where she was part of the Imperial College COVID-19 & Ebola response teams.  Her interdisciplinary research focuses on developing and applying novel statistical and mathematical models to inform global health policy.  Current projects involve developing spatio-temporal renewal type models to look at the role mobility plays in disease transmission as well as recovering transmission chains, and estimating the impact of crises on orphanhood and caregiver loss.

Dr Reluga received her PhD degree in 2020 from the University of Geneva. Before joining the School of Mathematics, she worked as a researcher at the University of Cambridge, the University of Toronto and the University of California, Berkeley. Her research interest lies at the intersection of survey methodology, causal inference and machine learning. During her PhD she worked on theoretical aspects of simultaneous, post-selection and computational inference with the applications in economics and social sciences. Afterwards, she broadened her research agenda by trying to solve some open problems in causal inference and merging machine learning with survey sampling methodology.