Also appears in collection : 2019 - T1 - WS3 - Imaging and machine learning

Electroencephalography (EEG), Magnetoencephalography (MEG) and functional MRI (fMRI) are noninvasive techniques that allow to image the active brain. Yet to do so, challenging computational and statistical machine learning problems need to be solved. As data are acquired everyday in both clinical and cognitive neuroscience contexts computations can become a bottleneck. In this talk I will present statistical inference problems relevant for neuroimaging (matrix factorization, sparse regression) and show how novel optimization strategies improve on the state-of-the-art.