While an explosion in data science research has fuelled enormous advances in areas as diverse as eCommerce and marketing, smart cities, logistics and transport, health and wellbeing, these tools have yet to be fully deployed in one of the most pressing problems facing humanity, that of mitigating and adapting to climate change.

Motivating Challenges

  • Ice Sheet Melt Prediction

    Accurate predictions of ice sheet change are crucial in planning global sea level adaptation and mitigation measures.

  • Air Quality Modelling

    Effective air quality modelling and mitigation is necessary to reduce the 3 million global deaths per year attributable to this cause.

  • Land Use Change

    In a changing climate, land use decisions must be made between food, timber, energy, recreation, urban settlement, employment and aesthetic benefits.

Methodological Themes

  • Integrated Statistical Modelling

    This theme will develop a suite of inference and prediction techniques for environmental problems, bringing together existing technology and new developments in a modular framework.

  • Machine Learning and Decision-Making

    We will enhance model-based statistics with modern machine learning techniques to ensure they scale to modern heterogeneous data sources and end-user requirements.

  • Virtual Lab Development

    This theme will focus on the development and deployment of virtual labs as a key instantiation of the integration of heterogeneous data and models that is central to our project.