EPSRC grant funding for Gabriel Brostow, Mark Girolami and Kate Jones (#EP/K015664/1)
Funded by EPSRC for 3 years
ENGAGE: Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research In short, it brings together Machine Learning, Computer Vision, Human-Computer Interaction, and Biodiversity Science to help cope with the global extinction crisis.
The PI's on the project are:
Gabriel Brostow - UCL CS
Mark Girolami - UCL Stats
Kate Jones - UCL Department of Genetics, Evolution and Environment Mike Terry - University of Waterloo, CS and ZSL Chair, Ecology and Biodiversity
Our vision is to establish and lead a new theme in ICT research based on Interactive Machine Learning (IML). Our expansion of IML will give scientists and non-ICT specialists unprecedented access to cutting-edge Machine Learning algorithms by providing a humancomputer interface by which they can directly interact with large scale data and computing resources in an intuitive visual environment. In addition, the outcome of this particular project will have a direct transformative impact on the sciences by making it possible for non-programming individuals (scientists), to create systems that semi-automatically detect objects and events in vast quantities of A) audio and B) visual data.
By working together across two parallel, highly interconnected streams of ICT research, we will develop the foundations of statistical methodology, algorithms and systems for IML. As an exemplar, this project partners with world leading scientists grappling with the challenge of analysing enormous quantities of heterogeneous data being generated in Biodiversity Science.