Short bio
PhD Ecole Normale Supérieure de Lyon (2011)
Research project
Compositional inference.
Short abstract
Approximate message passing algorithms, first applied to compressed sensing, are a promising approach to solve high-dimensional inference problems: their performance can be theoretically predicted and they often reach the optimal reconstruction error. We develop a modular package to ease the implementation of such algorithms: the user only has to declare the model then the inference, entropy estimation and prediction of performance are fully automated.