This research cluster develops AI, machine learning, and computational ecology methods to extract insight from large-scale environmental, ecological and biodiversity datasets. By combining ecosystem modelling, evolutionary and population dynamics, pathogen genomics, conservation science, and data-driven approaches to species interactions and environmental change, the groups uncover the mechanisms shaping biodiversity, disease transmission, ecosystem resilience, adaptation and extinction risk.
People
| Name | Quantitative ecology, big data and machine learning research |
|---|---|
| Lucy van Dorp | Developing computational genomic methods to reconstruct the evolutionary history and transmission pathways of bacterial and viral pathogens. |
| Kate Jones | Artificial intelligence tools for modelling ecosystems |
| Dan Maynard | Inferring patterns and developing theoretical models to predict ecological community dynamics. |
| David Murrell | Applying theoretical mathematical modeling and computational ecology to understand the spatial dynamics of populations and the mechanics of species coexistence in diverse landscapes. |
| Alex Pigot | Large datasets supporting models of extinction risk. |
| Gail Taylor | Using machine learning to understand plant genotype-to phenotype relationships in crops and trees, including remotely sensed data, molecular and morphophysiological traits and the leaf microbiome. |