Profiles

Professor Rue earned his Ph.D. in 1993 from the Norwegian University of Science and Technology. He began his academic career at the same institution in 1994 and was promoted to full professor in 1997. He has also held adjunct positions at the Norwegian Computing Center and the Arctic University of Norway. Rue is an elected member of the Norwegian Academy of Science and Letters, the Royal Norwegian Society of Science and Letters, the Norwegian Academy of Technological Sciences and the International Statistical Institute.

Upon joining KAUST in 2017, Rue established the Bayesian Computational Statistics & Modeling research group. The group develops efficient Bayesian inference schemes and tools to improve Bayesian inference and modeling using latent Gaussian models. He received the Guy Medal in Silver from the Royal Statistical Society in 2021 for his groundbreaking work in this area.

Professor Rue’s research interests lie in computational Bayesian statistics and Bayesian methodology, such as priors, sensitivity and robustness. His main body of research is built around the R-INLA project—a project aimed at providing a practical way to analyze latent Gaussian models at extreme data scales using approximate Bayesian analysis. The work also includes efforts to model Gaussian fields with stochastic partial differential equations, which are applied to spatial statistics.

Eman Kabbas earned a Bachelor of Science and Education in Mathematics from Imam Abdulrahman Bin Faisal University and a Master’s in Mathematics from the University of North Carolina at Charlotte. Her academic journey, marked by deep curiosity and dedication, led her to become a lecturer at Jubail Industrial College (JIC). Now, as a Ph.D. candidate in Applied Mathematics and Computational Sciences under the mentorship of Professor Håvard Rue, Eman delves into Bayesian and computational statistics, striving to bridge theoretical concepts with practical applications. Eman is dedicated to fostering a new way of teaching statistics and data science through her research experience.

Eman Kabbas's research interests focus on developing and applying spline models in non-parametric regression. She addresses the limitations of splines in prediction tasks with insufficient data by proposing a spline model suitable for both regular and irregular observations, leverages Bayesian techniques to ensure efficient modeling and reliable predictions.

Małgorzata Forystek is a Statistics Master's student at the Bayesian Computational Statistics & Modeling research group  (BAYESCOMP) under the supervision of Professor Håvard Rue. She received her B.Sc. degree in Applied Mathematics from AGH University of Science and Technology in Kraków, Poland, in 2023.

Her research interest is mainly in Bayesian and computational Statistics and Data Science.