Research activities in Signal processing and Statistics are inspired from data processing challenges in various application fields such as health engineering, nondestructive testing of materials, acoustics, remote sensing, astrophysics, transportation, electrical and mechanical engineering.
Our research aims at proposing solutions to big and possibly heterogeneous data analysis, statistical learning, data mining, temporally and spatially correlated signal analysis, optimal design of experiments, and inverse problems in signal and image processing. The proposed methods and algorithms rely on various tools such as multivariate statistics, numerical optimization, random matrix theory, sparse representation, and Bayesian inference. The group is also interested in Algorithm-Architecture Matching issues, at the interface between signal processing and High Performance Computing. This activity aims at fully exploiting the significant potential of parallel computing of signal processing algorithms on GPU and FPGA hardware accelerators.
RESEARCH TEAMS IN SIGNAL PROCESSING AND STATISTICS
The Inverse Problem Team is positioned at the interface between physics and statistical signal and image processing. The research work relies on variational and Bayesian approaches for signal and image reconstruction. The main application fields include audio, nondestructive testing, and astrophysics.
The Modelling and Estimation team aims to address massive data analysis problems based on mathematical tools such as multivariate statistics, robust statistics, and tensor analysis. The group is also interested in statistical approaches for optimal design of numerical experiments. The application fields include health, energy production, remote sensing, finance and statistical physics.