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The lab, headed by Wouter Saelens, uses advanced probabilistic AI models to solve complex immunological puzzles.
We focus on establishing 
links between genes and cell states. We apply key concepts from causal learning to go beyond correlations, and use multiplexed perturbational omics data where possible.
For this we use a 
perspective. Perturbational datasets give unique problems for inference and statistics, meaning we push the frontier of deep probabilistic models and amortization. Models are always validated in the lab; a true dream for any aspiring machine learner.
We apply this on urgent problems in 
, such as immune cell homeostasis, inflammation and oncology. We have a particular interest in macrophages, extremely abundant in these diseases, but to this date almost impossible to target. A causal systems approach to the rescue?
Our lab works closely together with the Center for Inflammation Research, enabling direct access to immunology tools and expertise.
Frequent interactions with VIB's core facilities, including the data core, single-cell core and spatial catalyst, provide our lab with forefront access to computational and experimental technologies.