Artificial Intelligence Lab

The BAS Artificial Intelligence Lab (or ‘BAS AI Lab‘) aims to foster the application of various machine learning (and adjacent) techniques to the rapidly growing, complex, and heterogeneous body of data found in atmospheric, oceanic, and Earth sciences.

The BAS AI Lab leads projects under the Alan Turing Institute’s Data Science For Science Programme, including Understanding Arctic sea ice loss. Through our PhD student projects we also collaborate closely with Google DeepMind, World Wildlife Fund, Max Fordham, Mott MacDonald and the UK Met Office.

PhD opportunities 

• Centre for Doctoral Training (CDT) in the Application of Artificial Intelligence to the study of Environmental Risks (Website, Twitter)

Current Student Projects

• Rachel Furner, BAS/Univ. Cambridge, Industry partner – Google DeepMind
  • PhD – New modelling tools for addressing computationally challenging climate science problems involving the ocean carbon cycle (2018-)
• Prem Gill, BAS/Univ. Cambridge, Industry partner – World Wildlife Fund (WWF)
  • PhD – Seals from Space: the study of Antarctic ice seals by remote sensing (2018-)
• Risa Ueno, BAS/Univ. Cambridge, Industry partner – Max Fordham
  • PhD – Machine learning for Megacities: Understanding the impact of future climate extremes on energy resilience (2018-)

Past talks and meetings

• The 1st Artificial Intelligence for Copernicus Workshop [video]
• The 6th Annual ICE, Heriot-Watt University and Stantec Resilience lecture, Edinburgh
• Seals from space: automated Antarctic ecosystem monitoring via high-resolution satellite imagery – Turing Data Study Group [video]
• How can machine learning techniques improve our understanding of climate change risks in the future?, Centre for Science and Policy, University of Cambridge
• “How to conduct a planetary health check”, Dr Emily Shuckburgh,  TEDxLeicesterSalon
• From Flatland to Our Land: A Mathematician’s Journey through Our Changing Planet
• The 2012 SIAM Annual Meeting [video]
• Data Analytics for Sustainability and Environmental Risk (​DASER 2016​)

Featured reports

• The uncertain unicycle that taught itself and how it’s helping AI make good decisions
• Machine learning: the power and promise of computers that learn by example