Research
Objectives
1
Propose graph models to represent spatio-temporal relationships in data
2
Develop GNN architectures tailored to spatio-temporal series
3
Validate the proposed approaches on real-world use cases (environment, health, transport)
Research axes
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Spatio-temporal graph construction
Development of methods to automatically construct graphs representing spatial and temporal relationships in data.
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Graph neural networks
Design of GNN architectures tailored to processing spatio-temporal series structured as graphs.
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Applications and validation
Validation of the approaches on real use cases in the fields of environment, health, and transport.