Department Systems Analysis, Integrated Assessment and Modelling

Data Science for Environmental Research

We use machine learning and statistical physics methods to tackle problems with large available datasets, and which involve a large number of interacting agents.

Ongoing research projects include:

  • Exploring the impact of toxines on aquatic organisms, predicting the effect of untested toxines on the species, guiding toxicological assessments.
  • The creation of a giant labeled dataset of plankton images and using it to infer the interactions among organisms, and between organisms and environment.
  • The study of the long-time dynamics of high-dimensional systems, from toy models, to deep neural networks and ecosystems.


Dr. Marco Baity Jesi Group Leader (he/him) Tel. +41 58 765 5793 Send Mail
Dr. Christoph Schuer Postdoctoral researcher Tel. +41 58 765 5684 Send Mail
Cheng Chen Tel. +41 58 765 5097 Send Mail


Dr. Marco Baity Jesi Group Leader (he/him) Tel. +41 58 765 5793 Send Mail


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
Community detection consists of extracting the affinity between agents of a system, which is extracted from quantities such as the frequency of interactions.
Activated dynamics is a very slow process that takes place on exponentially large time scales. Usually it is associated to barrier hopping.