The Signal and Statistics activity focuses on signal and image processing and statistical modeling. Research activities 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.
Signal processing methods rely on a wide range of mathematical tools such as multivariate statistics, numerical optimization, random matrix theory, sparse representation, Bayesian inference and tensor decomposition. This expertise allows us to propose 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. 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.