Dr. Ungar's research group develops scalable machine learning and text mining methods, including clustering, feature selection, and semi-supervised and multi-task learning for large bioinformatic and web-based problems. Example projects include semi-supervised methods for information extraction, multi-view learning for gene expression and fMRI data, and use of document and link structure for informing feature selection or transfer of knowledge between tasks.