Previously, he obtained his Ph.D. from the University of Illinois at Urbana-Champaign and was a post-doc at Stanford University . His research lies in the intersection of machine learning and natural language processing and is largely motivated by problems involving text understanding. In particular, he is interested in research questions related to developing semantic representations of text, learning discrete and real valued representations of textual inputs using little or incidental supervision, and efficiently predicting these representations. His work has been published at various NLP and machine learning venues and recently received the best paper award at EMNLP.