Department Systems Analysis, Integrated Assessment and Modelling

Machine Learning & Complex Systems


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.

Team

Dr. Marco Baity Jesi Group Leader (he/him) Tel. +41 58 765 5793 Send Mail
Emanuele Francazi Tel. +41 58 765 6410 Send Mail
Dr. Christoph Schuer Postdoctoral Scientist Tel. +41 58 765 5684 Send Mail
Cheng Chen Tel. +41 58 765 5097 Send Mail

Contact

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

Projects

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

Plankifier
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data

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

Exposure
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