My research primarily focuses on statistical machine learning models, with a strong emphasis on predictive tasks. My core interests include Bayesian methodologies and ensemble methods, although my work is not limited to these areas.

Over the past few years, I have developed and extended statistical methodologies to suit various contexts and assumptions. This has been complemented by my work in creating and maintaining R packages, which are openly accessible to the community, facilitating broader use and collaboration.

Currently, I am involved in the Project 4 Resilience initiative, where I apply statistical methodologies, particularly extreme value theory and spatio-temporal models, to enhance energy forecasting. This project aims to improve the resilience of energy systems by providing more accurate and reliable forecasts under varying conditions.

My research interests currently cover:

- Spatio-temporal modelling

- Bayesian Tree Models

- Ensemble models

- Statistical Learning Models