AIDOS LabRieck Lab
We are fascinated by discovering hidden structures in complex data sets, in particular those arising in healthcare applications.
Our primary research interests are situated at the intersection of geometrical deep learning, topological machine learning, and representation learning. We want to make use of geometrical and topological information—also known as manifold learning—to imbue neural networks with more information in their respective tasks, leading to better and more robust outcomes.
Following the dictum ‘theory without practice is empty,’ we also develop methods to address challenges in biomedicine or healthcare applications. Of particular interest are the analysis of MRI data sets to improve our understanding of human cognition and neurodegenerative disorders, as well as the analysis of multivariate clinical time series to detect and prevent the onset of sepsis or myocardial ischemia.
About our name
‘AIDOS’ has two meanings that complement each other well. The first meaning refers to our mission statement, viz. to develop Artificial Intelligence for Discovering Obscured Shapes. The second meaning originates from the Greek word ‘αἰδώς,’ which means ‘awe,’ ‘reverence,’ or ‘humility.’ This awe or humility should serve as one of our guiding principles when we work on challenging problems in healthcare research, aiming to improve our world using machine learning.
About our working culture
We are not interested in ‘leader-board science’ or ‘chasing the state-of-the-art’ in a table. That is not to say that we are not interested in producing relevant methods! Our overarching goal is to produce excellent science using methods whose performance we can explain and understand. This necessitates comprehensive comparisons with other methods, ablation studies, and many additional tricks to figure out what is going on. If this sounds enticing to you, we would love to hear from you! To learn more about our working style, see this note for potential student collaborators.