Ido Dagan has broad research experience and publication record in various areas of empirical natural language processing. He has presented several conference tutorials and summer school courses. In particular, Ido is interested in applied semantic modeling, facilitated largely through unsupervised learning approaches. In the last few years Ido and his colleagues introduced textual entailment as a generic framework for applied semantic inference and have been the main organizers of the first three rounds of the PASCAL Recognizing Textual Entailment Challenges.