remy kusters
Long term fellow
Remy's Bio

Hi, I am Rémy, a research fellow at CRI Research in Paris. My research interests are primarily the data-driven discovery of physical models from biophysical data. With a background in theoretical biophysics and soft matter physics I try to leverage the increased power of data science and in particular machine learning to discover physical models form experimental data.

I obtained a PhD from Eindhoven University of Technology and have been working as a postdoctoral researcher in Institute Curie. During my previous research experiences I have modeled the growth and physical regulation of dendritic spines and the mechanics of the cytoskeleton. Alongside I picked up some more generic soft matter projects, examples of which are the crowding induced clustering of active particles under confinement and the role of crowding in diffusion curved environments.

Growth and transport in the dynamical cytoskeleton
Growth and transport in the dynamical cytoskeleton
The AI network scientist
Classification of graphs using Neural Networks
Inferring antibiotic dose-response functions with deep learning
To automate the diagnosis of antibiotic resistance in bacteria.
Model discovery in physics and biology with neural networks
Using neural networks to automatically extract the differential equations underlying a data-set
Deep learning model discovery for viscoelastic materials
To use training of neural networks to discover the differential equations governing the stress and strain responses of uncharacterised viscoelastic materials.
Flemming Protocol
Aimed at creating a platform where scientists can collaborate, monetise and crowdfund their projects.