My research interest lies at the interface between statistical physics, computational neuroscience and computational biology, information theory and computer science. Its main themes include equilibrium and out-of-equilibrium phenomena in complex systems, statistical physics of inverse problems, statistical inference and combinatorial optimization, learning theory, probabilistic message-passing algorithms, stochastic processes, graphical games and coding.
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