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SciML Leeds Hackathon

Artificial Intelligence
Machine Learning
Science Machine Learning
Friday 30 June 2023, 12pm - 6pm
Hybrid - room 9.50, Worsley Building / MS Teams
Dr. Martin Rogers, British Antarctic Survey

Welcome to the SciML Leeds Hackathon! This exciting event will focus on using machine learning to identify the dominant ice-water interface via a fusion of SAR and optical imagery.

This hackathon is designed to be accessible to participants of all disciplines and skill levels. Whether you're a machine learning enthusiast or simply curious about its applications, this event is for you. You don't need to be an expert in ice or satellite imagery – we'll provide starter code to help you get started.

Exciting prizes will be up for grabs for the most innovative and impactful solutions. And to keep your energy levels up, we'll provide refreshments throughout the event. Join us on campus at LIDA or participate online from the comfort of your own home.

During the hackathon, you'll have the opportunity to work with a unique dataset provided by Dr. Martin Rogers from the British Antarctic Survey.

If you're interested in learning about machine learning and its applications in environmental science, this is the event for you. You'll have the chance to work with deep learning and collaborate with other participants to solve a challenging problem. We look forward to seeing you there!

Bio: Martin is a machine learning researcher in the Artificial Intelligence (AI) Lab at the British Antarctic Survey (BAS). His research primarily focusses on the application of machine learning techniques to detect features in satellite imagery, including multispectral visible and Synthetic Aperture Radar (SAR) datasets. Martin has previously trained and applied AI techniques, including convolutional neural networks, to automatically detect coastal features and land covers in satellite imagery. His current research focusses on the detection and classification of sea ice and other polar features in SAR imagery. Martin is also the project manager for the AI4EO Guided Team Challenges, which forms part of the taught element of the Application of AI to the study of Environmental Risks (AI4ER) CDT.