Research Group at the University of Bonn and Lamarr Institute
The Geometry in Machine Learning group works at the intersection of machine learning, computer vision and computer graphics. Our research focuses on developing novel techniques that leverage geometric insights to enhance the capabilities of machine learning models. By studying the underlying geometric structures of data, we aim to create more robust, efficient, and interpretable algorithms. Our work spans a wide range of topics, including geometric representation learning, classic geometry processing, and learning under symmetry, as well as applications in physics.
By Lio Schmitz, Markus Plack, Berkan Koyak, Muhammad Ehsan Ullah, Ahmad Aziz, Reinhard Klein, Zorah Lähner, Hannah Dröge
Published in Pacific Symposium on Biocomputing (PSB).
By Daniele Baieri, Filippo Maggioli, Emanuele Rodolà, Simone Melzi, Zorah Lähner
Published in Conference on Neural Information Processing Systems (NeurIPS).
By Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Martin Kliesch, Michael Moeller
Published in arxiv:2507.12536.
By Viktoria Ehm, Nafie El Amrani, Yizheng Xie, Lennart Bastian, Maolin Gao, Weikang Wang, Lu Sang, Dongliang Cao, Zorah Lähner, Daniel Cremers, Florian Bernard
Published in Symposium on Geometry Processing (SGP).
By Paul Roetzer, Viktoria Ehm, Daniel Cremers, Zorah Lähner, Florian Bernard
Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
By Aleksei Zhuravlev, Zorah Lähner, Vladislav Golyanik
Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
