Probabilistic machine learning as an algorithmic interface to weather model and environmental data

Add to your list(s) Download to your calendar using vCal

• Charlie Kirkwood | University of Exeter
• Tuesday 09 March 2021, 11:00-12:30
• https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09.

If you have a question about this talk, please contact Tudor Suciu.

As the volume of data we collect and generate skyrockets, how can we maximise the utility of this data for the purposes of environmental science and management? In this talk I will outline the challenge of our current situation, and why I think probabilistic machine learning should be part of the solution (I get the feeling you won’t be a tough crowd on this point!). I will share examples from my research at the University of Exeter and the Met Office, including quantile regression forests for weather forecast post-processing, and Bayesian deep learning for end-to-end modelling of environmental variables – atmospheric and lithospheric. The insights from these projects contribute to the idea of ‘algorithmic interfaces’ as key to the future provision of environmental information.

This talk is part of the AI4ER Seminar Series series.

This talk is included in these lists:

• AI4ER Seminar Series
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Interested Talks
• Trust & Technology Initiative - interesting events
• bld31
• https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.