Ecology informed machine learning

As biodiversity declines and anthropogenic pressure increases, there is a crucial need for accurate ecosystem models that can capture the dynamics of real ecosystems in order to predict and mitigate collapses. Yet it remains a daunting task to obtain an agreement between current ecosystem models and real world ecosystems. In this project we aim at bridging machine learning techniques and mechanistic models in order to extrapolate beyond data. By embedding prior scientific knowledge in the structure of mechanistic models, this approach should generalise better, be more interpretable and require less data than current techniques.

Victor Boussange
Victor Boussange
PhD candidate

Researcher in ecology and evolution, scientific machine learning enthusiastic.