Rue honored by Royal Statistical Society
KAUST Professor of Statistics Håvard Rue has been honored by the Royal Statistical Society (RSS) for his "substantial and significant contributions in the area of efficient and scalable computational techniques for the data analysts' toolbox."
"I am delighted to see Professor Rue's outstanding research being recognised by the RSS in this way. Håvard is one of the stars of the excellent Statistics faculty team that we have at KAUST, of whom we are justly proud," Professor Donal Bradley, Vice President of Research said.
Rue has been formally honored with the Guy Medal in Silver for 2021. The Medal is awarded to a Fellow of the Society in respect of a paper or papers of special merit communicated to the Society at its discussion meetings. The RSS intends to recognize Rue and his co-awardees during the Society's Annual Conference in Manchester on Tuesday September 7, 2021.
Substantial and significant contributions
Rue has been honored specifically for the theory underpinning the INLA (integrated nested Laplace approximation) software. His contributions include two highly influential and impactful papers read before the society: "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" (co-authors: S Martino and N Chopin) and "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach" (co-authors: F Lindgren and J Lindström).
"KAUST is extraordinarily proud of the accomplishments of Professor Håvard Rue. This recognition from the Royal Statistical Society is reflective of the technical excellence and impact of his research," said Lawrence Carin, KAUST Provost. "The INLA methodology has made a significant contribution for the practical implementation of Bayesian techniques, via its accuracy and computational efficiency. We are excited for Professor Rue, and we look forward to his continued creativity and impact."
The papers focused on computational modeling issues, providing scalable techniques and practical model fitting solutions. The first paper lays the foundation of the integrated nested Laplace approximation approach for latent Gaussian models permitting the fitting of complex methods quickly and accurately, which has revolutionized the area of spatial statistics, and has been applied to areas ranging from gene expression to public health. The second paper describes the relationship between continuously indexed Gaussian fields and discretely indexed Gaussian Markov random fields leading to significantly simpler model-fitting.
About Professor Håvard Rue
With affiliations in Statistics, as well as Applied Mathematics and Computational Science, Rue has a wide-range of research interests. Specifically the work of his team delves into computational Bayesian statistics and Bayesian methodology such as priors, sensitivity and robustness.
His main body of research is built around the R-INLA project (www.r-inla.org), which aims to provide a practical tool for approximate Bayesian analysis of latent Gaussian models, often at extreme data scales. This project also includes efforts to use stochastic partial differential equations to represent Gaussian fields, for the use in spatial statistics.
About the RSS
The Royal Statistical Society was founded in 1834 as a professional body for all statisticians and data analysts, wherever they may live. The RSS has more than 10,000 members in the UK and across the world. As a charity, they advocate for the key role of statistics and data in society, and work to ensure that policy formulation and decision making are informed by evidence for the public good.