Welcome to the lab page of the Data Mining and Modeling for Biomedicine group (Saeys lab). Our research is situated at the intersection of machine learning, AI, biomedicine and immunology, with a particular focus on developing and applying state-of-the-art machine learning methodology for single-cell data.

Our lab is affiliated to two institutions: at Ghent University we are part of the Department of Applied Mathematics, Computer Science and Statistics, and at VIB we are part of the VIB Center for Inflammation Research.

Research Topics:

Machine learning for single-cell “omics”
  • Trajectory inference
  • Dynamic regulatory network inference
  • Benchmarking single-cell methods
Machine learning and bio-image informatics
  • Robustness and interpretability of modern machine learning techniques
  • Automated segmentation of 3D-EM imaging
  • Machine learning for image analysis
Translating computational flow cytometry to the clinic
  • Automated gating/visualization
  • Biomarker discovery
  • Clinical applications
Modeling gene regulation using multi-omics data integration
  • Upstream regulator analysis
  • Multimodal data integration
  • Modeling intercellular communication