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

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

Network analysis: I have developed a novel model and scalable inference algorithm for inferring community structure and predicting missing links in email networks with an unbounded number of individuals. With one of my graduate students, I have developed methods for modeling social networks in terms of the documents sent between individuals, leading to improved performance over existing approaches.

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, and am working on ways of distributing inference in a streaming setting.

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.

Deep Learning: I am working with several of my graduate students on new deep learning algorithms, including generative adversarial networks (GANs) aimed at combatting inherent data bias; Bayesian methods for uncertainty quantification in deep networks; and ensuring interpretability in auto encoders.


Sinead Williamson

Contact Details


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