Research

The AIDOS lab is dedicated to establishing foundational principles in machine learning. Leveraging our experience in computational geometry and topology, we focus on shaping well-principled methods to address holes in the rapidly evolving AI landscape.

We see ourselves as toolsmiths, crafting both observational and interventional frameworks using concepts such as the Euler characteristic transform, metric space magnitude, discrete curvature, persistent homology, and many more. Whether working with graphs, images, or natural language, our goal is to build tools that shed light on the most difficult questions, prioritizing simplicity, elegance, and interpretability over mere performance.

We hope our work can give back to the community, empowering new research directions and application developments grounded in principled methods. Although our core research targets method development in machine learning, we are also passionate about impacting change with the help of our wonderful collaborators in biomedical, healthcare, and environmental applications.

Publications

Here are all publications of lab members, sorted by year. Publications appear in the order in which they are accepted.