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Our activities in probability and statistics are organized around the following themes:

• Development of machine learning methods (supervised or unsupervised) suited to specificities of digital agriculture data, phenotyping and agroecology. We are particularly interested in taking into account the expertise, the integration of heterogeneous data to develop robust learning methods.
• Extension of sensitivity analysis methods to complex outputs of agronomic models.

• Design and analyses of stochastic algorithms and branching models motivated by applications in population ecology and microbial ecology.

We are careful to disseminate our results through R packages or tutorials for fellow biologists.

Permanent staff:

• Bertrand Cloez
• Bénédicte Fontez
• Nadine Hilgert
• David Metivier
• Sébastien Roux
• Isabelle Sanchez
• Nicolas Verzelen (coordinator)