David Bolin
- Associate Professor, Statistics
- Principal Investigator, Stochastic Processes and Applied Statistics
David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics.
Biography
David Bolin is an associate professor of statistics in the CEMSE Division at KAUST, where he leads the Stochastic Processes and Applied Statistics research group. Before joining KAUST, he was an associate professor of Mathematical Statistics at the University of Gothenburg. He received his Ph.D. in mathematical statistics from Lund University in 2012.
Bolin's research focuses on stochastic partial differential equations (SPDEs) and their applications in statistics, with an emphasis on developing practical, computationally efficient tools for modeling non-stationary and non-Gaussian processes. He has made significant contributions to the theory of Gaussian processes, optimal linear prediction, fractional-order SPDEs, and stochastic processes on metric graphs. He has also developed and maintains several widely used software packages for advanced statistical modeling.
Bolin serves as an associate editor for the Scandinavian Journal of Statistics, is an elected member of the International Statistical Institute, and has received multiple honors, including the Section on Statistics and the Environment Early Investigator Award from the American Statistical Association and the Cramér Prize from the Cramér section of the Swedish Statistical Society.
Research Interests
Professor Bolin’s main research interests are stochastic partial differential equations (PDEs) and their applications in statistics, focusing on developing practical, computationally efficient tools for modeling non-stationary and non-Gaussian processes.
The Swedish researcher leads the Stochastic Processes and Applied Statistics (StochProc) research group at KAUST, which focuses on statistical methodology for stochastic processes and random fields based on stochastic PDEs.
This research combines methods from statistics, probability and applied mathematics in order to construct more flexible statistical models and better computational methods for statistical inference. In parallel with the theoretical research, the group works on applications in a wide range of areas, ranging from brain imaging to environmental sciences.
Education
- Doctor of Philosophy (Ph.D.)
- Mathematical Statistics, Lund University, Sweden, 2012
- Master of Science (M.S.)
- Engineering Mathematics, Lund University, Sweden, 2007
Quote
In the Stochastic processes & applied statistics group, we develop statistical models and methods involving stochastic processes and fields for a wide range of applications.
Selected Publications
- Bolin, D. ., & Kirchner, K. . (2022). Necessary and sufficient conditions for asymptotically optimal linear prediction of random fields on compact metric spaces. Annals of Statistics , 50(2), 27. Retrieved from https://projecteuclid.org/journals/annals-of-statistics/volume-50/issue-2/Necessary-and-sufficient-conditions-for-asymptotically-optimal-linear-prediction-of/10.1214/21-AOS2138.full (Original work published 2022)
- Bolin, D. ., Simas, A. B., & Wallin, J. . (2024). Gaussian Whittle-Matérn fields on metric graphs. Bernoulli, 30(2), 27. Retrieved from https://projecteuclid.org/journals/bernoulli/volume-30/issue-2/Gaussian-WhittleMatérn-fields-on-metric-graphs/10.3150/23-BEJ1647.short (Original work published 2024)
- Boin, D. ., & Lindgren, F. . (2015). Excursion and Contour Uncertainty Regions for Latent Gaussian Models. Journal of the Royal Statistical Society, Series B, 77(1), 21. Retrieved from https://academic.oup.com/jrsssb/article/77/1/85/7040622 (Original work published 2015)