Welcome to the AIDOS Lab
We are a research group at the University of Fribourg , working at the intersection of geometry, topology, and machine learning .
Grounded in mathematics, we build principled methods that reveal hidden structure in complex data.
If you are a student at the University of Fribourg and are interested in writing a bachelor’s or master’s thesis with us, please drop us a line .
What We Do
Data has shape, and we build tools that find it.
Our core focus is geometrical and topological machine learning , i.e., developing methods that make use of principles from geometry and topology to learn robust, expressive representations. We work with concepts like Euler Characteristic Transforms , persistent homology , discrete curvature , and metric space magnitude to analyze point clouds, graphs, and manifolds.
We see ourselves as toolsmiths
Our research is graciously supported by the Canton of Fribourg and the Swiss State Secretariat for Education, Research and Innovation SERI, which funds our ERC Starting Grant “HOLES: Higher-Order Learning of Essential Structures with Geometry and Topology” under the transitional measures for the Horizon package 2021–2027.
Why We Do It
“AIDOS” carries two meanings. The first one describes our work, i.e., A rtificial I ntelligence for D ata-O riented S cience. The second one originates from the ancient Greek word “αἰδώς,” which denotes a sense of awe, reverence, or humility when facing something greater than oneself. This awe keeps us honest about the many things we do not (yet) know, and we strongly believe that this is the right disposition for doing science.
News
May 27, 2026 :
🎉 Our work on EmbedOR: Provable Cluster-Preserving Visualizations with Curvature-Based Stochastic Neighbor Embeddings
April 11, 2026 :
🎉 Our work on TOAST: Transformer Optimization using Adaptive and
Simple Transformations
April 8, 2026 :
Rubén defended his dissertation “Topology-Enhanced Deep Learning” with a cum laude certification.
He has joined Axiom to focus on theorem
proving and AI for Mathematics. Congratulations and all the best—it
was an honor working with you!
March 6, 2026 :
Julius defended his dissertation “Robust Topological Representation
Learning” with magna cum laude and is the first PhD student of the
lab to graduate . Congratulations and all the best for your future
career, Julius! It was a pleasure working with you!
January 26, 2026 :
🎉 Our work on LEAP: Local ECT-Based Learnable Positional Encodings
for Graphs
January 2, 2026 :
🎉 Our work on Molecular Machine Learning Using Euler Characteristic
Transforms
December 19, 2025 :
👏 Our academic visitor Giacomo (Parolin) defended his MSc thesis
“Differentiable Euler Characteristic Transform for Molecular
Prediction” with the highest possible exam grade “cum laude.”
Congratulations!
October 24, 2025 :
🎉 Jeremy’s paper Strategies to accelerate US coal power phase-out
using contextual retirement
vulnerabilities
October 15, 2025 :
🎉 Our research on geometry-aware edge
pooling , LLM analysis using
intrinsic dimensionality , and
point cloud generation was
accepted at NeurIPS 2025. Our small lab has now been present in all
of the three major machine learning conferences since its foundation
in 2022. May Fortuna continue smiling upon us! 🍀
October 1, 2025 :
🐣 The lab is taking shape! With Richard, Nadja, Johannes, Kavir, and
Martin joining as Ph.D. students, we are now eagerly awaiting our
postdocs Elena and
Inés . Welcome,
everyone!
August 1, 2025 :
🎉 Johannes’s paper Stable and Accurate Orbital-Free Density
Functional Theory Powered by Machine
Learning
July 22, 2025 :
🎉 Bastian’s article Topology Meets Machine Learning: An Introduction Using the Euler Characteristic Transform Notices of the AMS is out.
If you are wondering what this is all about, here are brief summary
posts on X
and BlueSky .
May 21, 2025 :
🎙 Bastian was interviewed on the DataSkeptic podcast
to discuss our work on No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets .
May 1, 2025 :
🎉 Our works on local variants of the Euler Characteristic Transform
and on a principled analysis of graph-learning data sets
have been accepted at ICML 2025!
February 1, 2025 :
🎉 Our works on molecule generation
and on new data sets for topological deep learning
have been accepted at ICLR 2025!
January 8, 2025 :
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 .
December 19, 2024 :
Emily wrote up a great thread on SCOTT , our
new codebase for curvature filtrations.
See her post on X or
BlueSky
for more details.
