Environment Community
The recently formed LIDA Environment theme has started fostering cross-university collaboration between researchers who apply machine learning in their day-to-day research in the physical sciences at the University of Leeds. This has crystallised around the Scientific Machine Learning (SciML) Community, connecting early-career machine learning practitioners.
Over the past year, the SciML Community has hosted numerous events:
- Monthly informal ML meetups on the first Friday of each month at 11am, open to all researchers.
- Hybrid seminars focused on machine learning applications in climate, point cloud processing, explainable AI, and more. External speakers have included researchers from eg. Imperial, MIT, Princeton.
- PhD student-led workshops on physics-informed neural networks and reinforcement-learning.(Future plans include workshops on equifinality in different models and semi-supervised learning.)
- A summer hackathon where 30 researchers dived into the exciting world of machine learning applied to environmental science. In this one-day event, the participants worked with satellite imagery provided by the British Antarctic Survey to identify the ice-water interface of sea ice.
The group has compiled resources to support new ML researchers including GPU access, coding tutorials, and best practices for running models. (https://sciml-leeds.github.io)
Furthermore, it has expanded its open-source training materials on applying ML in the physical sciences (https://cemac.github.io/LIFD_ENV_ML_NOTEBOOKS/).
Moving forward, the SciML Community plans to continue growing its seminar series and workshops. There are goals to increase cross-department collaboration on projects utilising ML for environmental modeling, weather prediction, and other domains.