Małgorzata Łazȩcka

The paper introduces FACTMx, a variational framework that jointly models structured and simple patient-level data to learn interpretable representations, showing improved performance in simulations and applications to cancer and cardiovascular cohorts.

This study identifies distinct pneumonia endotypes based on pathogen-specific host responses in the lung, linking them to clinical outcomes and suggesting personalized treatment strategies.

CLING is a multi-view factor model that improves factor and loading recovery in multi-omic data by using nonparametric Gamma shrinkage, outperforming baselines on synthetic and real datasets.