I received my PhD in Statistics from the University of Washington in 2011, and was a Postdoctoral Research Fellow at the Statistical Laboratory in Cambridge from 2011 to 2013.

My research is in understanding how multivariate statistical models – such as Bayesian network models, marginal models and latent variable models – can be used to learn about the world around us. In addition, I am interested in how these methods can be applied to epidemiology, medicine and the social sciences.

This work has led in various directions: finding implications of models that can be tested in data, and showing that no further constraints exist; determining whether quantities of scientific interest are identifiable; understanding when marginal models can be properly specified, simulated from, and fitted; and understanding when efficient model selection is possible.  A particular area of interest recently has been simulating from marginal causal models, and applications to combining evidence from different types of study.

I am also interested in the mathematical and statistical properties of more general latent variable models, conditional independence, and model parametrizations.