Overview

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.

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Publications

_{Copyright Notice: Any articles made available for download are for personal use only. Any other use requires prior permission of the author and the copyright holder.}

| 2023 | 2022 | 2021 | 2020 |

2023

• Angelov A, Atanasov A, Atanasov VA, Gyoshev SB, Saraivanova J, Volodina V. (2023) Circulatory Disease and the Wide Sex and Ethnic Life Expectancy Gaps in Bulgaria since 2010.
• Sonenberg N, Volodina V, Challenor PG, Smith JQ. (2023) Using infinite server queues with partial information for occupancy prediction, Journal of the Operational Research Society, volume 75, no. 2, pages 262-277, DOI:10.1080/01605682.2023.2189002.
• Sonenberg N, Volodina V, Challenor PG, Smith JQ. (2023) Using infinite server queues with partial information for occupancy prediction. [PDF]

2022

• Volodina V, Wheatcroft E, Wynn H. (2022) Comparing district heating options under uncertainty using stochastic ordering, SUSTAINABLE ENERGY GRIDS & NETWORKS, volume 30, article no. ARTN 100634, DOI:10.1016/j.segan.2022.100634. [PDF]
• Volodina V, Sonenberg N, Wheatcroft E, Wynn H. (2022) MAJORIZATION AS A THEORY FOR UNCERTAINTY, INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, volume 12, no. 5, pages 23-45, DOI:10.1615/Int.J.UncertaintyQuantification.2022035476. [PDF]
• Volodina V, Sonenberg N, Smith JQ, Challenor PG, Dent CJ, Wynn HP. (2022) Propagating uncertainty in a network of energy models, 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), DOI:10.1109/pmaps53380.2022.9810635.

2021

• Wheatcroft E, Wynn HP, Volodina V, Dent CJ, Lygnerud K. (2021) Model-Based Contract Design for Low Energy Waste Heat Contracts: The Route to Pricing, ENERGIES, volume 14, no. 12, article no. ARTN 3614, DOI:10.3390/en14123614. [PDF]
• Volodina V, Challenor P. (2021) The importance of uncertainty quantification in model reproducibility, Philos Trans A Math Phys Eng Sci, volume 379, no. 2197, DOI:10.1098/rsta.2020.0071. [PDF]
• Couvreux F, Hourdin F, Williamson D, Roehrig R, Volodina V, Villefranque N, Rio C, Audouin O, Salter J, Bazile E. (2021) Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement, Journal of Advances in Modeling Earth Systems, volume 13, no. 3, DOI:10.1029/2020ms002217. [PDF]
• Hourdin F, Williamson D, Rio C, Couvreux F, Roehrig R, Villefranque N, Musat I, Fairhead L, Diallo FB, Volodina V. (2021) Process‐Based Climate Model Development Harnessing Machine Learning: II. Model Calibration From Single Column to Global, Journal of Advances in Modeling Earth Systems, volume 13, no. 6, DOI:10.1029/2020ms002225. [PDF]

2020

• Volodina V, Williamson D. (2020) Diagnostics-driven nonstationary emulators using kernel mixtures, SIAM-ASA Journal on Uncertainty Quantification, volume 8, no. 1, pages 1-26, DOI:10.1137/19M124438X.

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