Sinead Williamson joined The University of Texas at Austin in 2013. She was a Senior Research Scientist with Amazon, Inc. and a Lead Machine Learning Scientist with CognitiveScale. Her main research focus is the development of nonparametric Bayesian methods for machine learning applications. In particular, she is interested in constructing distributions over correlated measures and complex structures, in order to model structured data sets or data with spatio-temporal dependence. Examples include models for documents whose topical composition varies through time, and models for temporally evolving social networks. A key research goal is the development of efficient inference algorithms for such models, and she is currently investigating methods that allow us to apply Bayesian nonparametric techniques to large datasets. She has worked as a Senior Research Scientist with Amazon, Inc. and a Lead Machine Learning Scientist with CognitiveScale, and is on the Board of Directors of Women in Machine Learning. 

Ph.D. in Engineering (Machine Learning), University of Cambridge, 2012