I’m interested in fundamental aspects as well as in the development of probabilistic message passing algorithms of different degrees of complexity, with applications to inverse problems that arise in computational biology and theoretical neuroscience.

Our activity in computational biology has led us to set up real quantitative biology experiments.

Basic topics:

- Statistical physics, stochastic processes
- Inverse problems
- Distributed algorithms for optimisation, constraint satisfaction problems and statistical inference
- Information Theory
- Graphical games

Computational Biology:

- Modeling post-transcriptional regulation: theory and experiments (!)
- Protein contacts inference
- Pathways identification
- Inverse problems from single cell genomics

Computational Neuroscience:

- Statistical physics of learning and deep learning
- Neuromorphic engineering:
*in situ*learning in hardware for large scale problems - Input supervised learning in neural systems
- Hardness of learning in neural networks with constrained “material” synapses