David Bolin

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David Bolin

David Bolin

@jdavidbolin

Professor of statistics at @cemseKAUST

Katılım Aralık 2014
69 Takip Edilen187 Takipçiler
David Bolin retweetledi
KAUST CEMSE
KAUST CEMSE@KAUST_CEMSE·
From autonomous vehicles to environmental monitoring, #KAUST Statistics Professor David Bolin’s research focuses on developing statistical methods that make sense of complex, structured data. Leading the Stochastic Processes and Mathematical Statistics group, Bolin and his team work at the interface of probability theory and real-world systems through stochastic partial differential equations (#PDEs). His research addresses challenges where traditional statistical assumptions break down, such as traffic data constrained by road networks and environmental data shaped by ocean dynamics. These methods support applications ranging from traffic safety analysis, with the potential to inform evidence-based policy decisions, to early-warning models for ecological change in the Red Sea. By pairing methodological advances with accessible software, Bolin’s work emphasizes adoption, ensuring that theory translates into tools that can guide research, planning and policy. Read more about how Bolin is bridging mathematical statistics and real-world impact: cemse.kaust.edu.sa/articles/2026/… #CEMSE #KAUSTStatistics #ReadSea #SmartCities #RedSeaResearch #StochasticPDEs #DataScience
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David Bolin
David Bolin@jdavidbolin·
The paper on the approach that facilitates using SPDE models with arbitrary smoothness in @bayescomp_inla and @inlabru is now out in JCGS. The method recently won one of the 2023 KAUST competitions on Spatial Stats for Large Data, so it works really well. tandfonline.com/doi/full/10.10…
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David Bolin retweetledi
R-INLA
R-INLA@bayescomp_inla·
Huge congratulations 👏👏 to @Rafael_M_Cabral for successfully passing his PhD defense. He was advised by @jdavidbolin and @HavardRue1 and has made great contributions to latent non gaussian models.
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David Bolin
David Bolin@jdavidbolin·
Great work by Rafael Cabral (student of Håvard Rue and me at @cemseKAUST) on fitting latent non-Gaussian models.
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Andy Seaton
Andy Seaton@ASeatonSpatial·
@millerdl abstract looks interesting. Although I thought you cannot identify marg var, range and differentiability param all at once + I don't have an intuition for estimating differentiability, don't I need data at "infinitesimal" resolution? @dan_p_simpson @FinnLindgren
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David Bolin retweetledi
Mandy Mejia, PhD
Mandy Mejia, PhD@mandyfmejia·
Our paper on an ICA model for fMRI using surface-based spatial priors & empirical population priors was accepted at JCGS! Using spatial priors dramatically improves power to identify areas of the brain engaged in different functional networks. @jdavidbolin arxiv.org/abs/2005.13388
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David Bolin
David Bolin@jdavidbolin·
@dan_p_simpson Thanks!! But some might argue that it is a great day when you need to prove your own Sobolev embedding theorem. :)
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David Bolin
David Bolin@jdavidbolin·
New paper about Gaussian processes on metric graphs, such as street networks. With @Jonas_Wallin and Alexandre Simas. This took a long time to develop, but I am really happy about the outcome. R implementations for INLA etc are coming soon. arxiv.org/abs/2205.06163
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