Biomedical Data Analysis Group

Welcome

We work on machine learning and deep learning methods, with a focus on biomedical applications. Our activities include the development of statistical models for prediction, feature selection, clustering, network inference, but also image segmentation and causality mining. Currently we have research projects on identifying soil degradation in the Swiss Alps, modelling bird migration, and discovering novel molecules with desirable properties. In previous research projects we worked on EEG analysis in the context of Parkinson dementia, on DNA sequence analysis for HIV therapy optimization, on copula distributions and on causal models.

News

02 August 2024

Manuel Jahn joined our group as a new PhD student.

03 June 2024

Our paper on "Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations" got accepted in the Journal of Computational Science (Elsevier) for the special issue "Machine Learning and Data Assimilation for Dynamical Systems".

02 April 2024

Shijian Xu joined our group as a new PhD student.

16 January 2024

Our paper on "Lagrangian Flow Networks for Conservation Laws" (paper, OpenReview) got accepted at the International Conference on Learning Representations (ICLR 2024), taking place in Vienna Austria, 7th to 11th May 2024.