Pantelis Samartsidis

The paper introduces a scalable Bayesian factor analysis framework for handling mixed and incomplete data, demonstrating its effectiveness on MS datasets.

distinct identifies differential distribution patterns in single-cell data by comparing empirical cumulative distribution functions, outperforming existing methods in detecting subtle changes.

The paper estimates the prevalence of missing non-significant experiments in a neuroimaging meta-analysis using a zero-truncated model applied to BrainMap data.