Robin Freeman leads the Conservation AI Lab at ZSL's Institute of Zoology.
Dr Robin Freeman is a Senior Research Fellow at the Institute of Zoology, ZSL, where he leads the Conservation AI Lab, developing new approaches to predicting biodiversity change. His research focuses on understanding and predicting global biodiversity change using ecological data science, machine learning and AI.
His work focuses on developing methods that integrate diverse ecological datasets, from long-term population monitoring to emerging data streams such as biologging and automated sensing. This approach improves how biodiversity trends are measured, understood and forecast, and supports more effective conservation decision-making.
Robin has played a key role in the development and application of global biodiversity indicators, including the Living Planet Index, produced in partnership with WWF, which tracks trends in vertebrate populations worldwide.
He originally trained in machine learning and zoology at Oxford, and continues to develop new analytical approaches for studying species behaviour, population dynamics and responses to environmental change. His research has contributed to major international assessments and reports, informing conservation policy and practice.
Robin collaborates widely with academic institutions, NGOs and international organisations, and has previously worked with Microsoft Research Cambridge, UCL and the University of Oxford. He supervises postgraduate students and contributes to MSc and PhD training in biodiversity, conservation science and ecological data science.
- Using AI and machine learning to understand and predict biodiversity change
- Forecasting how ecosystems respond to global environmental change
- Bringing together different types of ecological data, from long-term monitoring to newer data sources
- Building practical data pipelines for conservation research
- Animal behaviour, biologging and conservation
- Collective behaviour
Some selected recent publications:
- Swaby, L. et al. (2026). Deep neural networks to predict foraging behaviour in seabirds. Journal of the Royal Society Interface
- Capdevila, P. et al. (2026). Halting predicted vertebrate declines requires tackling multiple drivers of biodiversity loss. Science Advances
- Alexander, E. J. et al. (2026). Sociality and seabird diving in gannets. Marine Ecology Progress Series
- Mac Aodha, O. et al. (2018). Deep learning tools for bat acoustic detection. PLoS Computational Biology
- Leclère, D. et al. (2020). Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature
See a full list of Robin's publications here: https://robinfreeman.github.io/publications.html