Tom is an ML Research Scientist at the British Antarctic Survey (BAS) AI Lab, where he researches and develops ML systems for monitoring and adapting to climate change. His work currently focusses on the application and implementation of neural processes in environmental sciences. Tom has used uncertainty quantification, interpretability, and active learning methods to build decision-support tools and his previous work includes IceNet, a sea ice forecasting AI system.
In this webinar, Tom will present recent advances and applications of neural processes (NPs) in environmental sciences. NPs are versatile deep learning models which can tackle a diverse array of environmental prediction problems, including sensor placement, downscaling, forecasting, and infilling missing satellite data. This versatility is enabled by modelling flexibility: NPs can ingest arbitrary sets of observations of point-based or gridded modalities, predict at arbitrary locations, and quantify prediction uncertainty. However, the flexibility of NPs can make them poorly suited to small-data settings, and open questions remain about how to optimise their performance. To answer these questions and accelerate research, Tom is developing an open-source Python package for environmental NP modelling, DeepSensor, which will be presented in this webinar.
Further information and details, this and the wider webinar series is online at https://digitalenvironment.org/webinars/cde-webinar-series-upcoming/#andersson.