Conservation AI LabAI • ecological prediction • animal behaviour • biodiversity monitoring
Current Members
Placeholder portrait for Robin Freeman

Robin Freeman

Senior Research Fellow

Robin leads the Conservation AI Lab at the Institute of Zoology, Zoological Society of London. His research sits at the intersection of artificial intelligence, ecological prediction, biodiversity indicators, animal behaviour, and conservation decision-making.

robin.freeman@ioz.ac.uk
GitHub · ORCID · Google Scholar

Conservation AI Biodiversity Change Animal Behaviour
Philippa Oppenheimer

Philippa Oppenheimer

PhD student / Post-doc

Predicting global threats to wildlife populations and improving the data underpinning biodiversity indicators.

Threats Indicators Global change
Daan Scheepens

Daan Scheepens

PhD student

Understanding and predicting the impacts of land use and climate on arthropod biodiversity and consequences for global agriculture

LLMs Pest Control Food security Global change
Rudai (Fred) Zan

Rudai (Fred) Zan

PhD student

Understanding the lagged responses of biodiversity to climate and land-use change.

Ecological lags Climate Land use
Dylan Carbone

Dylan Carbone

PhD student

Deep learning to predict subject depth in camera trap images.

Deep learning Camera traps Computer vision
Phoebe Santos

Phoebe Santos

Masters student

Understanding how language models can be used to transcribe and extract ecological knowledge from historical archives.

LLMs Historical archives
Phoebe Santos

Aoao Guo

Masters student

Developing a computer vision system for individual identification of Humboldt penguins, using deep learning to recognise penguins from photographs.

Deep learning Computer vision Pattern recognition
Alumni and Collaborators

Ruth Dunn

Ruth Dunn

Collaborator

Seabird movement ecology, energetics, and influences on marine ecosystems.

Seabirds Energetics Movement Ecology
Alice Trevail

Alice Trevail

Collaborator

Using animal tracking data to understand how species interact with their environment and to support conservation decision-making.

Seabirds Movement Ecology Spatial Modelling
Valentina Marconi

Valentina Marconi

Collaborator / PhD student (completed)

Machine learning to improve global biodiversity indicators, predicting extinction risk and wildlife abundance.

Machine learning Indicators Extinction risk
Hannah Wood

Hannah Wood

Collaborator / PhD student (completed)

Seabird movement, behaviour and conservation planning.

Seabirds Movement ecology Conservation
Aline Da Silva Cerqueira

Aline Da Silva Cerqueira

Collaborator / PhD student (completed)

Seabird Soundscapes, acoustics and machine learning.

Seabirds Machine Learning Conservation

Group overview

16+ PhD students supervised to completion
28+ MRes or MSc students supervised to completion
Open To collaborations, students and project partnerships
Get in touch! robin.freeman@ioz.ac.uk