BioSiS - Multidimensional Signals (SiMul)

The project's objective is to develop methodological research in signal processing, image processing and data science with a focus on machine learning and AI. The work concerns the design of interpretable models that faithfully represent the observed multidimensional data while maintaining reasonable complexity, typically linear with the number of dimensions. It also involves developing efficient algorithms, with guarantees, for reconstructing, inverting and deciding directly from models of reduced complexity. The general scope of these developments allows for repercussions through two main application axes: biology and digital health, chemometrics and signal processing from physical measurements. This work is carried out in collaboration with other projects in the BioSiS department and with national and international academic or industrial partners.