I use machine learning and statistical physics methods to tackle problems with large available datasets, and which involve a large number of interacting agents. I am also interested in the interaction between dynamics and energy / loss function landscape in systems with a large number of variables, ranging from toy models to deep neural networks.
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We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
We test a big data workflow for understanding and predicting plankton dynamics using monitoring data.
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data
Activated dynamics is a very slow process that takes place on exponentially large time scales. Usually it is associated to barrier hopping.
We use machine learning methods to predict the effects of chemicals on aquatic species.
Community detection consists of extracting the affinity between agents of a system, which is extracted from quantities such as the frequency of interactions.