Stock photo of the earth with visualisation of a network overlaid

About Me

Duncan Watson-Parris

I'm an atmospheric physicist working at the interface of climate research and machine learning. Having obtained a PhD in theoretical physics from the University of Manchester I became a software consultant, providing bespoke data analysis for researchers across industry and academia. Since moving back into academia, I have applied these skills to investigate the effect of anthropogenic aerosols on the climate.

Using cutting-edge machine learning techniques to combine global aerosol models with novel observational constraints I look to better understand these effects and improve projections of climate change. I'm also keen to foster the application of machine learning to climate science questions more broadly and am Course Director of the iMIRACLI Innovative Training Network, convene the Machine Learning in Climate research forum within the University of Oxford and co-hosted the recent Climate Informatics 2020 conference.

Recent work

A selection of recent projects I've been working on; a more comprehensive list can be found under Projects.

In the News

This article from the Wall Street Journal explores the role of Amazon, Microsoft and Google in meeting the need for cloud-scale computing to tackle some of the largest climate research problems we face. It highlights my work on detecting ship-tracks in vast quantities satellite imagery using Machine Learning on Amazon Web Services.

In this podcast Philip Stier and I discuss the use of cloud computing for tackling some of the most pressing climate change questions, including for hosting large machine learning workflows such as the detection of pockets of open cells. This was also featured in a blog post by Amazon CTO Werner Vogels.

This article in Science magazine highlights our work using neural architecture search to develop fast, flexible emulators that are applicable to a wide range of physical simulators. These emulators are often orders of magnitude faster than the simulators they replicate and can allow solving inverse problems - that is, determining model inputs based on matching observable outputs.


Please feel free to get in touch if you have any questions about my work or are interested in collaboration.