About Me

I am an Associate Professor of Statistics at the University of Texas at Austin, in the Department of Statistics and Data Science. 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

My research interests span several areas in Machine Learning and Statistics. Key areas include:

Network analysis: I am interested in building models that capture realistic behavior in interaction-based networks. Bayesian nonparametric priors allow us to model networks of unbounded size, while capturing commonly seen graph properties such as sparsity and power law behavior. I have developed models for clustered interaction networks, clique-based binary networks, and temporally evolving graphs, and continue to explore new ways of capturing relational and interaction data.

Scalable inference methods: Developing faster, distributed Bayesian inference algorithms has been a long-term research direction of mine. I have developed theoretically justified parallel MCMC algorithms for a number of challenging Bayesian nonparametric models, plus approximate scalable inference algorithms for both parametric and nonparametric models.

Bayesian nonparametrics: The nonparametric Bayesian paradigm is an elegant and flexible approach for modeling complex data of unknown latent dimensionality, and was the focus of much of my PhD and postdoctoral work. In particular, I am interested in dependent nonparametric processes - distributions over collections of measures indexed by values in some covariate space.


Sinead Williamson

Contact Details


Statistics & Data Science
WEL 5.228
105 E 24th St, D9800
Austin, TX 78705