This is an idea proposed in 2024 as a Cambrige Computer Science Part III or MPhil project, and is available for being worked on. It will be supervised by Anil Madhavapeddy, Sadiq Jaffer, Alec Christie and Bill Sutherland as part of my Conservation Evidence Copilots project.
The Conservation Evidence Copilots database contains information on numerous conservation actions and their supporting evidence. We also have access to a large corpus of academic literature detailing species presence and threats which we have assembled in Cambridge in collaboration with the various journal publishers.
This MPhil project aims to combine these published literature resources with geographic information to propose conservation interventions. The goal is to identify actions that are likely to be effective based on prior evidence and have the potential to produce significant gains in biodiversity. This approach should then enhance the targeting and impact of future conservation efforts and make them more evidence driven.
To realize this project, several key components need to be developed, each of which could constitute an MPhil project in its own right:
If you're interested in applying machine learning and LLM techniques to global conservation, then get in touch about the above or any other ideas you might have.