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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.
Discovery of Significant Emerging Trends
as author at Industry / Government Sessions ,