Søren Hauberg

Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision.

• Department: Applied Mathematics and Computer Science
• Research Group: Cognitive Systems
• Institution: Technical University of Denmark (DTU)

His research interests center on geometric deep learning, Bayesian inference, and manifold-based modeling. He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems.

The recent publications reveal a strong trend toward geometric representation learning, fairness in generative models, and computational methods for biological data. His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation.

Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science, indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling.

He is involved in several active research projects, including:

• Geometric Bayesian Deep Learning
• Stochastic Riemannian Geometry with Applications to Data Modelling
• The geometry of protein representations
• AI-driven Electron Tracking for High-Energy Radiation Detection