I am an Assistant Professor in the Department of Biostatistics at Johns Hopkins University.
My research is in causal inference and epidemiologic methods. Broadly, I am interested in developing methods for and describing the behavior of traditional statistical machinery when standard assumptions are not met. I have worked on characterizing the bias that results from misclassification, i.e. violations of the assumption that variables were measured accurately. I have also worked on semiparametric estimation of instrumental variables models, as these models are useful for certain violations of “no unmeasured confounding” assumptions.
Currently a major focus of my work is on analysis of social and other network data. I am working on methods for statistical inference when observations are dependent, with a dependence structure informed by network topology rather than Euclidean topology, and on how to identify causal effects when treatments exhibit interference (that is, when one subject’s treatment may affect other subjects’ outcomes) and outcomes exhibit contagion.
I am a member of the causal inference working group.