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Jordan Boyd-Graber is a postdoc working in the Computational Linguistics and Information Processing group at the University of Marlyand Institute of Advanced Computer Studies. He is working with Philip Resnik on multilingual sentiment analysis, among other things. Before that, he was a graduate student in the Machine Learning Group at Princeton working with David Blei.
He is interested in applying statistical models (particularly latent variable approaches) to natural language applications. His PhD thesis was on adding linguistic assumptions to topic models (one example of a latent variable model for text).
Reading Tea Leaves: How Humans Interpret Topic Models
as author at Conference Sessions,