AI for Environmental Sciences
21 July, 2019 by Rachel Furner
Rachel Furner is a PhD student at British Antarctic Survey, which has recently opened up its new AI Lab, that aims to foster the application of various machine learning (and …
Artificial Intelligence Lab
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.
PhD opportunities
- Centre for Doctoral Training (CDT) in the Application of Artificial Intelligence to the study of Environmental Risks (Website, Twitter)
Research areas
- AI for Climate
- Model bias correction and downscaling [Scott Hosking]
- AI for Oceans
‘Precipitous fall in #Antarctic sea ice’ @guardian: https://t.co/zi9ChuieYq. @BAS_News researcher @PolarPrem discusses the Turing #DataStudyGroup challenge to monitor Antarctic sea ice & seal population: https://t.co/0rvyCZCWA1. GET INVOLVED in #TuringDSG: https://t.co/6Er63bSCKC pic.twitter.com/MHNlM8pLIx
— The Turing (@turinginst) July 2, 2019
Other relevant groups
- Cambridge Big Data Strategic Initiative
- Cambridge Centre for Climate Science
- Centre for the Study of Existential Risk, University of Cambridge
- Centre for Science and Policy, University of Cambridge
- University of Cambridge Institute for Sustainability Leadership
- Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge
Past talks and meetings
- Data Sciences for Climate and Environment, The Alan Turing Institute
- How can machine learning techniques improve our understanding of climate change risks in the future?, Centre for Science and Policy, University of Cambridge
- Environmental science in the big data era, 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
Graduate Students
- Will Tebbutt, Univ. Cambridge, Industry partner – Google DeepMind
- PhD – Bayesian Machine Learning for the calibration of climate model ensembles (2017-)
- Robert Rouse, Univ. Cambridge, Industry partner – Mott MacDonald
- Rachel Furner, BAS/Univ. Cambridge, Industry partner – Google DeepMind
- 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
Former Members
Tom Andersson (MEng Part IIB Dissertation, Machine Learning Group, Cambridge, 2019), Shahel Khan (Part III Project, Univ. Cambridge, 2017-2018), Stratis Markou (Internship, Machine Learning Group, Cambridge, 2017), Lille Borresen (Internship, 2018), Daniel Popa-Christobal (Internship, 2018), Harry Holt (paper, Internship, 2017)
External collaborators
- Richard Turner, Department of Engineering, University of Cambridge
- Alex Archibald, Department of Chemistry, University of Cambridge
- Peter Haynes, Department of Applied Mathematics and Theoretical Physics, University of Cambridge
- Ian Leslie, Department of Computer Science and Technology, University of Cambridge
- Mukesh Kumar, Institute for Manufacturing, University of Cambridge
- Mark Webb, Met Office
- Stephen Briggs, Department of Chemistry, University of Cambridge
Preprint available for preview and comment: Unsupervised clustering of Southern Ocean Argo float temperature profiles
7 June, 2018 by Dan Jones
What can machine learning tell us about the structure of the Southern Ocean? In this preprint, we apply unsupervised clustering, a machine learning method, to Southern Ocean temperature data. The …
Policy workshop summary: The impacts of machine learning on climate change modelling
7 June, 2018 by Dan Jones
Summary available here: http://www.csap.cam.ac.uk/news/article-climate-change-modelling/
Using AI to help tackle global environmental challenges
26 February, 2019
A new Centre for Doctoral Training, involving researchers from British Antarctic Survey, will develop Artificial Intelligence (AI) techniques to address critical environmental challenges. Climate change and environmental hazards pose some …
Funding to assess shipping emissions and climate hazards
13 December, 2018
British Antarctic Survey (BAS) scientists have been awarded funding to enable them to assess the impact of emissions from shipping and to quantify and manage the risk of climate hazards. …
Watching whales from space
1 November, 2018
Scientists have used detailed high-resolution satellite images provided by Maxar Technologies’ DigitalGlobe, to detect, count and describe four different species of whales. Reported this week in the journal Marine Mammal …
European wind energy generation potential in a 1.5˚C world
17 May, 2018
The UK and large parts of northern Europe could become windier if global temperatures reach 1.5˚C above pre-industrial levels, according to a new study. This has implications for wind energy …
FEATURED PAPER: Temperature change in Sichuan
13 November, 2017
The Sichuan basin is one of the most densely populated regions of China. Along with insufficient arable land and economic underdevelopments, this region is particularly vulnerable to climate-related stresses. Improving …
Climate Scientist awarded Prestigious Mathematical prize
18 July, 2017
Last week (12th July) Dr Emily Shuckburgh travelled to Pittsburgh, USA, and received the prestigious 2017 I. E. Block Community Lecture prize from SIAM – the Society for Industrial and …