Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.
Throughout the world, organic waste generation is posing serious challenges, threatening food security and water purity and availability. Saudi Arabia is no exception.
Prof. Raphaël Huser has been appointed to serve as an Associated Editor for the Journal of the Korean Statistical Society (JKSS) starting on January 1, 2020.
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).
A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.
New accepted paper: Castro Camilo, D., and Huser, R. (2019+), Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes, Journal of the American Statistical Association, to appear.