I am a data scientist and R/Shiny developer with a PhD in Ecology, currently based in Nancy, France. I specialize in ecological forecasting, species distribution modeling (SDM), and the development of interactive applications that empower scientists to work more efficiently. I’m passionate about bridging the gap between complex modeling and user-friendly tools.
My Shiny App: TuneSDM
TuneSDM is an advanced interactive app that helps users build, evaluate, and visualize Species Distribution Models using machine learning. It supports model tuning (grid/Bayesian), spatial transfer, and ensemble prediction. Designed for ease of use, it eliminates coding barriers through an intuitive Shiny interface enriched with HTML, CSS, and JavaScript enhancements.
The app supports multiple models: Random Forest, XGBoost, GLM, MaxEnt, SVM, Neural Networks, MARS, GAM, and KNN. It handles presence/absence data, supports SpatRaster layers for environmental predictors, and automates the entire workflow from tuning to final mapping.
You can view a short video demo of TuneSDM here:
R Package & Scientific Work
I developed an R package built on top of {tidymodels} to automate model tuning. My academic work includes 11 peer-reviewed publications, focusing on ecological dynamics, stress-tolerance in plant communities, and biotic interactions across gradients. My projects often merge statistical rigor with practical tooling.