My broad interests are in the intersection of computational linguistics and statistical learning. I am most interested in predicting structure from text. Such structure underlies natural language at all its levels, from discourse, through semantics to syntax.

My current interests, all interacting at some level, are: (1) the use of linear algebra for learning statistical models, especially those with hidden variables that are not observed in the data; (2) semantic representations, especially abstract meaning representation and discourse representation structures, and their applications such as document summarization. In my work I use various statistical learning algorithms and language formalisms, including neural networks, spectral methods, probabilistic grammars and others.

BSc Mathematics and Computer Science, 2000, Tel Aviv University, School of
Exact Sciences
MSc Computer Science, 2004, Tel Aviv University, School of Exact Sciences
PhD Language and Information Technologies, 2011, Carnegie Mellon
University, School of Computer Science

Shay Cohen is a Reader at the University of Edinburgh (School of Informatics). Prior to this, he was a postdoctoral research scientist in the Department of Computer Science at Columbia University, and held an NSF/CRA Computing Innovation Fellowship. He received his B.Sc. and M.Sc. from Tel Aviv University in 2000 and 2004, and his Ph.D. from Carnegie Mellon University in 2011. His research interests span a range of topics in natural language processing and machine learning, with a focus on structured prediction (for example, parsing) and text generation.