About Me

Head of the group Data Science for Environmental Research, at the  Department of Systems Analysis, Integrated Assessment and Modelling.

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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Projects

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).

Contrasting patterns and processes of biological invasions in blue versus green ecosystems

We test a big data workflow for understanding and predicting plankton dynamics using monitoring data.

Plankifier

Deep neural networks (DNNs) have shown impressive empirical performance but they are still nevertheless a black-box function modeling data.

Universal Aspects of Deep Neural Networks

Activated dynamics is a very slow process that takes place on exponentially large time scales. Usually it is associated to barrier hopping.

Entropic Activation

We use machine learning methods to predict the effects of chemicals on aquatic species.

ML Tox

Community detection consists of extracting the affinity between agents of a system, which is extracted from quantities such as the frequency of interactions.

Exposure

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Curriculum Vitae

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