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Welcome

Welcome to the website of the AIDOS Lab at the University of Fribourg. We are fascinated by discovering hidden structures in complex data sets, in particular those arising in healthcare and environmental applications.

Our primary research interests are situated at the intersection of geometry, topology, and machine learning. From data-centric evaluation frameworks to novel architecture developments, we make use of geometrical and topological information to imbue our methods with foundations that foster interpretability and robustness. Along the way, we develop new manifold learning techniques, new representation learning algorithms, and much more.

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Following the dictum ’theory without practice is empty,’ we address challenges in biomedicine and healthcare applications. Check out our research page to learn more about our current projects.

Mission Statement

‘AIDOS’ has two meanings that complement each other well. The first meaning refers to our mission statement, viz. to develop Artificial Intelligence for Data-Oriented Science. 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, aiming to improve the world using machine learning.

News

: Bastian will give three talks at JMM, the Joint Mathematics Meetings, one on “Diss-lECT: Dissecting Data with local Euler Characteristic Transforms” (related to a recent preprint of ours), the second one on “Two Households, Both Alike In Dignity: Geometry and Topology in Machine Learning,” and the final one on “Good Gradients and How To Find Them: Towards Multi-Scale Representation Learning.” Find these (and more!) talks at Bastian’s website.
: Emily wrote up a great thread on SCOTT, our new codebase for curvature filtrations. See her post on X or BlueSky for more details.