Dr Victoria Volodina

My research interests span Bayesian statistics, machine learning and decision theory. My work primarily focuses on developing new methodology and new algorithms for decision support systems using varying mathematical techniques including Uncertainty Quantification methods, time-series models and graphical models. My theoretical research has been applied to climate science, the public sector and healthcare.

Bibliographical information

I obtained my PhD in Mathematics in 2019 from the University of Exeter under the supervision of Dr Daniel Williamson. My PhD research was focused on developing new approaches for quantifying uncertainties for complex computer modelsof physical systems. From 2019 to 2021, I was a research associate on the project" Managing Uncertainty in Government Modelling" (MUGM) at the Alan Turing Institute, where I was interested in developing approaches to study and account for uncertainty in models used to inform decisions in public policy using various mathematical techiques including majorisation, infinite server queues and graph theory. Prior to joining Exeter, I was a research fellow in computational statistics on a collaborative project in mathematics, engineering and AI, CHIMERA (UCL), one of four national hubs for mathematics and healthcare. I mainly worked on graphical models to perform imputation about the clinical variables monitored in critically ill patients using the monitor data.