Congratulations to the winners of the Canadian Journal of Statistics Award for their article entitled “Fast and Scalable Inference for Spatial Extreme Value Models.”

This article develops a computationally efficient Bayesian inference framework for spatial generalized extreme value models by combining Laplace approximation with a sparsity-inducing spatial covariance technique. The proposed methodology achieves substantial gains in scalability while maintaining strong inferential accuracy, as demonstrated through simulation studies and a real-data application.

Meixi Chen

Meixi Chen completed her PhD and MMath in statistics at the University of Waterloo, and her BSc in mathematics and statistics at the University of Toronto. Her doctoral research focused on approximate Bayesian computation with applications in meteorological and biological data. 

Reza Ramezan

Reza Ramezan is an associate professor, teaching stream, with an active research program in the Department of Statistics and Actuarial Science at the University of Waterloo. Prior to joining Waterloo, he was an assistant professor of statistics at California State University, Fullerton. He earned his PhD in statistics from the University of Waterloo in 2014. His main research interests lie at the intersection of statistics and neuroscience, focusing on multivariate point processes, computational and high-dimensional statistical methods, multiscale modelling, machine learning, and latent variable models.

Martin Lysy

Martin Lysy is associate professor and director of the Statistical Consulting and Survey Research Unit in the Department of Statistics and Actuarial Science at the University of Waterloo. Martin's research interests include modelling of biophysical and spatiotemporal processes, as well as computational methods in statistics and machine learning.