PhD student
Team : MLIA
Arrival date : 04/03/2017
Localisation : Campus Pierre et Marie Curie
    Sorbonne Université - LIP6
    Boîte courrier 169
    Couloir 26-00, Étage 5, Bureau 525
    4 place Jussieu
    75252 PARIS CEDEX 05
Tel: +33 1 44 27 51 29, Daniel.Brooks (at)
Supervision : Matthieu CORD

Deep learning for radar signals

This PhD aims at developing new deep framework for radar signals. In this context, covariance matrices have attracted attention for machine learning applications due to their capacity to model meaningful statistical properties of the data. The main challenge is that one needs to take into account the particular geometry of the Riemaniann manifold of symmetric positive definite (SPD) matrices they belong to. In the context of neural networks, we propose in this PhD to study new deep architectures for handling these SPD matrices. We plan to introduce new projection layers based on the Riemaniann barycenter of the data, or other kinds of projections that could be learned.
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