​

Affiliations

• Statistics
• Applied Mathematics and Computational Science

Education Profile

• ​​​​​Postdoctoral Research Fellow, King Abdullah University of Science and Technology 2014-2015
  
• Ph.D. Statistics, Swiss Federal Institute of Technology (EPFL), 2013
• M.Sc. Applied Mathematics, Swiss Federal Institute of Technology (EPFL), 2009
• B.Sc. Mathematics, Swiss Federal Institute of Technology (EPFL), 2007

Research Interests

​Professor Huser's main research interests lie at the intersection between statistics of extreme events, risk assessment, spatio-temporal statistics, and statistical approaches for large datasets, with particular focus on environmental applications such as the prediction of extreme flooding, droughts, and wind gusts. He develops statistical models for rare events, as well as efficient inference methods to fit them to data.

Selected Publications

• Huser, R., Opitz, T., and Thibaud, E. (2020+), Max-infinitely divisible models and inference for spatial extremes, Scandinavian Journal of Statistics, DOI: 10.1111/sjos.12491, to appear
• Lombardo, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R. (2020), Space-time landslide predictive modeling, Earth-Science Reviews 209, 103318
• Castro Camilo, D., and Huser, R. (2020), Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes, Journal of the American Statistical Association 115, 1037-1054
  
• Vettori, S., Huser, R., and Genton, M. G. (2019), Bayesian modeling of air pollution extremes using nested multivariate max-stable processes, Biometrics 75, 831-841
• Huser, R. and Wadsworth, J. (2019), Modeling spatial processes with unknown extremal dependence class, Journal of the American Statistical Association 114, 434-444
• Huser, R., and Davison, A. C. (2014), Space-time modeling of extreme events, Journal of the Royal Statistical Society: Series B 76, 439-461