My primary research interest is in unsupervised learning of linguistic structure, both by humans and by machines. Historically I have worked mainly with probabilistic (especially Bayesian) models, which are useful for exploring the kinds of structures and constraints that are needed to support linguistic generalizations. More recently I have also been working with neural network models, and models combining NN and Bayesian aspects. Areas I am particularly interested in include
Sharon Goldwater is a Reader (similar to a US Associate Professor) in the Institute for Language, Cognition and Computation at the University of Edinburgh's School of Informatics. She received her PhD in 2007 from Brown University, supervised by Mark Johnson, and spent two years as a postdoctoral researcher at Stanford University before moving to Edinburgh. Her research interests include unsupervised learning for natural language processing, computer modelling of language acquisition in children, and computational studies of language use. Dr. Goldwater holds a Scholar Award from the James S McDonnell Foundation for her work on "Understanding synergies in language acquisition through computational modelling" and is the 2016 recipient of the Roger Needham Award from the British Computer Society for "distinguished research contribution in computer science by a UK-based researcher who has completed up to 10 years of post-doctoral research." Dr. Goldwater has sat on the editorial boards of several journals, including Computational Linguistics, Transactions of the Association for Computational Linguistics, and OPEN MIND: Advances in Cognitive Science (a new open-access journal). She co-chaired the 2014 Conference of the European Chapter of the Association for Computational Linguistics (EACL) and is now chair-elect of the EACL governing board.