Let’s discover the world of the statisticians at KAUST and be ready to be amazed by their cutting-edge research!
Prof. Haavard Rue was invited as a keynote speaker to the "Advancing knowledge about spatial modeling, infectious diseases, environment and health" virtual workshop organized by the Fields Institute for Research in Mathematical Sciences, during June 2020.
Prof. Haavard Rue was invited as a keynote speaker at the International Conference on Mathematics, Computational Sciences, and Statistics (ICoMCoS) 2020, organized by Universitas Airlangga in Indonesia during 29-30 September.
Together with Dr. Janet van Niekerk, they presented a virtual workshop on R-INLA and its applications.
Prof. Rue is in London for a few days in December for the 12th International Conference of the ERCIM WG on Computational and Methodological
Statistics (CMStatistics 2019), and a visit to research collaborators at
the London School of Hygiene & Tropical Medicine, as well as some collaborators at Imperial College.
Prof. Haavard Rue has been acknowledged as one of the World's most highly cited researchers on the Highly Cited Researchers 2019 list from the Web of Science Group.
Dr. Anna Freni-Sterrantino from Imperial College in London is visiting during 2-23 November, Dr. Fatima Batool from University College in Lis visiting during 16-26 November and Dr. Erica Ponzi from the University of Oslo in Norway is visiting during 9-23 November.
Dr. Haakon Bakka gave a presentation at the Norwegian Water Resources and Energy Directorate on 25 September 2019, on the research performed at KAUST with Prof. Luigi Lombardo and Prof. Raphael Huser on Landslide modelling using R-INLA.
The 3 faculty positions are in the Statistics Program (http://stat.kaust.edu.sa) within the Computer, Electrical, and Mathematical Sciences and Engineering Division. Currently, the Statistics Program has 7 core faculty and 10 affiliated faculty. We are primarily interested in applicants with strong background in one of the following areas: (1) Statistical Data Science and AI, including network data analysis and high-dimensional statistics (https://apply.interfolio.com/69165); (2) Statistical Climatology, with expertise in statistical analysis of climate model output data, in particular regional climate models, and in physical systems (https://apply.interfolio.com/69167); (3) Statistics for Public Health, including smart health data analysis, personalized medicine, and disease mapping (https://apply.interfolio.com/69168).
Prof. Haavard Rue presented a seminar at OsloMet's Artificial Intelligence Group on 23 September. He also continued some existing collaboration.
Dr. Janet van Niekerk presented a talk on complex survival models in the International Symposium in Computational and Methodological Statistics and Biostatistics in July 2019 at the University of Pretoria. She also collaborated on existing and new projects with collaborators from South Africa and North Carolina.
Prof. Haavard Rue, Dr. Haakon Bakka and Dr. Janet van Niekerk attended and presented at the International Workshop on Statistical Modelling in July 2019. This workshop brings together experts and young researchers to discuss and collaborate on challenges in statistical modelling.
Prof. Haavard Rue was at Oxford in May 2019 for a research visit and to examine a PhD thesis of one of Prof. Chris Holmes’ students.
The BayesComp group hosted visiting student Maria Bondareva through the VSRP program from January to June 2019. She worked on projects concerning survival models and joint models and developed some vignettes for survival analysis in R.
Prof. Haavard Rue visited the Carlos III University in Madrid, Spain during May 2019 to give a seminar at the Department of Statistics and to collaborate on ongoing projects.
Prof. Haavard Rue was an invited speaker at the Swiss Numerics Day 2019 meeting in May 2019 at USI Lugano, Switzerland. He contributed to this meeting with a talk about the role and importance of sparse matrices in statistics. He also collaborated with Prof Olaf Schenk, which is the brain behind the PARDISO sparse matrix library in R, and others.