Proposal Description

The increasing availability of heterogeneous data across environmental and societal domains calls for robust statistical frameworks capable of integrating information from multiple sources, resolutions, and measurement systems. This session will bring together researchers developing and applying innovative methods for statistical data fusion and integration, highlighting both theoretical advances and real-world applications.

Talks will span methodological foundations, such as hierarchical and spatial models for combining multi-source information, as well as applications in ecology, climate science, and health. The session will illustrate how modern statistical approaches can bridge data from different domains, linking remote sensing, in-situ observations, citizen science, and administrative records, to improve inference, prediction, and decision-making in complex environmental systems.

By drawing together complementary perspectives, this session aims to foster dialogue between statisticians and domain scientists, identifying common challenges and future directions for multi-source modelling and cross-disciplinary data integration.

Proposed speakers and their research focuses:
• Finn Lindgren (University of Edinburgh) – Methodological foundations (integrating multi-source spatial information)
• Sebastian Mutz (University of Glasgow) – Climate and environmental modelling
• Jafet Belmont Osuna (University of Glasgow) – Ecological applications
• Carles Milà Garcia (Barcelona Supercomputing Center) – Cross-domain health & environment