Independent Component Analysis (ICA) provides a principled framework for unsupervised feature extraction and blind source separation, with ubiquitous applications in signal processing, astronomy and neuroimaging. In the multi-view setting, the aim is to extract common sources of variability from multiple related observations. In recent years, a novel theory of nonlinear ICA was developed, urging the need for new estimation methods. In the course of the exchange, I worked on the development of new algorithms for the estimation of Independent Components for single and multi-view ICA, both for the linear and nonlinear case. These algorithms will be applied to the modeling group studies in neuroimaging.