Researcher in Agroecology, contributing to a better quantitative understanding of environmental processes. Focus on developing data-driven solutions to mitigate the impact of agriculture by understanding the variability of soil functionality.

Recognising soil as a key agroecosystem regulator, my research employs data-driven applied statistics to understand its complex functions. I rely on transparent, data-driven applied statistics. My core principle is embracing diversity across environments and methods, aiming to comprehend multivariate variability, rather than control it. This vision grounds ecological understanding in rigorous, evidence-based, methodological development.

Teaching role focused on strengthening data infrastructure and statistical modelling capabilities. As a lecturer, I specialise in R programming, with a pedagogical approach that promotes the practical application of statistical principles, prioritising hands-on data analysis. This mentorship framework also guides my supervising role of PhD candidates in agro-environmental modelling.

Statistical consultancy delivered to research groups, ag-tech companies, governmental institutions, and NGOs within the agricultural and soil sciences. Core services include experimental design, model specification, spatial analysis, model auditing, and sensitivity analysis. 

Currently, my work focuses on modelling soil organic carbon sequestration and nitrate leaching. My approach overall integrates advanced statistical frameworks to support evidence-based decision-making, mainly in a Danish context.