I am a Researcher at the Swedish University of Agricultural Sciences, working at the interface of mathematical statistics, ecological data analysis, and forest applications. I hold a PhD in Mathematical Statistics.
My expertise includes regression models, generalized linear and mixed-effects models, spatial and spatio-temporal point processes, Monte Carlo simulation, kernel-based methods, and reproducible statistical workflows in R. I have experience working with large-scale environmental and forest datasets, including Swedish National Forest Inventory data (NFI).
In addition to research, I teach regression models to bachelor’s and master’s students, with emphasis on statistical inference, model interpretation, and implementation in R.
My research focuses on statistical modelling and inference for spatial and spatio-temporal data, with applications in environmental and forest sciences. I am particularly interested in point process models, separability testing, spatial interaction, temporal dependence, simulation-based inference, and computational methods for complex ecological datasets.
At SLU, I work with Swedish National Forest Inventory and related monitoring data to study ecological and environmental processes. My current research includes statistical analysis of ant mound occurrence and moose browsing damage in young Swedish forests. These projects combine regression modelling, mixed-effects models, spatial data analysis, and computational methods to better understand forest ecosystem patterns and their relationships with environmental covariates.
I also develop reproducible workflows and statistical tools in R, including methods for simulating and testing spatio-temporal point process models. My broader research interests include forest dynamics, carbon sequestration, ecological monitoring, machine learning methods in statistics, and the development of statistical methods that can support sustainable forest management.
My work in environmental analysis focuses on applying statistical modelling and computational methods to ecological and forest data. I have experience analysing large-scale environmental datasets, including Swedish National Forest Inventory data, with applications to forest dynamics, ant mound occurrence, moose browsing damage, and spatial-temporal ecological processes.
I use regression models, mixed-effects models, spatial and spatio-temporal statistics, simulation-based inference, and R programming to study relationships between ecological patterns and environmental covariates. My research aims to support a better understanding of forest ecosystems and contribute to sustainable forest management.