Data dimensionality informs us about data complexity and sets limit on the structure of
successful signal processing pipelines. In this work we revisit and improve the manifold …

Many approaches in the field of machine learning and data analysis rely on the assumption
that the observed data lies on lower-dimensional manifolds. This assumption has been …

The machine learning tasks of supervised classification and unsupervised noise detection
are commonly performed separately. In this paper, we propose a combination of both tasks …

Neural data analysis has many faces and every novel signal processing approach opens up
new possibilities to explore brain anatomy and function. In this thesis I take a deep dive into …