Bio

Victoria Volodina is a research associate at the Turing working as part of the project Managing Uncertainty in Government Modelling (MUGM). Victoria has received her PhD in Mathematics from the University of Exeter, where she was supervised by Dr Daniel Williamson. Her PhD thesis titled “Uncertainty Quantification for complex computer models with nonstationary output. Bayesian optimal design for iterative refocussing”, presents the Uncertainty Quantification (UQ) tools developed to assist robust calibration of complex computer models such as climate models.

Victoria’s work is in the areas of Uncertainty Quantification and Bayesian statistics. Her research interests include emulation, history matching, design of computer experiments and majorisation with application to climate and energy models.

Research interests

As part of MUGM, Victoria is interested in developing innovative mathematical methods for quantifying uncertainty in government models.