About Me

I am an Assistant Professor of Statistics at the University of Texas at Austin, in the IROM Department and the Division of Statistics and Scientific Computation. I obtained my PhD from the Computational and Biological Learning group at the University of Cambridge, and spent two years as a post doc in the SAILING laboratory at Carnegie Mellon University.

Research interests

I am interested in nonparametric Bayesian methods for use in machine learning applications. The nonparametric Bayesian paradigm is an elegant and flexible approach for modeling complex data of unknown latent dimensionality. In particular, I am interested in dependent nonparametric processes -- distributions over collections of measures indexed by values in some covariate space. Such models are appropriate for spatio-temporally variable data, and for sharing information between related tasks. I am also interested in the development of fast and scalable inference algorithms for Bayesian nonparametric models.


September 2014 Our paper on dynamic nonparametric trees has been accepted at NIPS. Preprint to follow shortly!
June 2014 Our paper on parallel inference for Pitman-Yor processes and related models has been accepted at UAI. Check it out here!
Apr 2013 Our paper on restricting exchangeable nonparametric distributions has been accepted at NIPS.
Apr 2013 I will be joining the McCombs Business School at UT Austin as an Assistant Professor in Statistics, in August 2013.
Feb 2013 Our paper on dependent random measures has been selected as a Notable Paper at AISTATS. Check out the preprint.

Sinead Williamson

Contact Details


CBA 6.476
McCombs School of Business
1 University Station, B6000
Austin, TX 78712