Bayesian inference; statistical demography; global child, maternal, and reproductive health.
715 North Pleasant Street
Amherst, MA 01003
My primary research focus is the development of statistical models to assess and interpret demographic and population-level health trends and differentials, generally on a national level for all countries globally, but also subnationally, focusing on developing countries. I have developed estimation and projection methods for key global indicators, including child and maternal mortality rates, the total fertility rate, and the unmet need for contraceptive methods. One of the main methodological challenges in this area of research arises from the need to capture data-driven trends in data-rich populations, while also producing reliable estimates and projections for populations where data availability is limited and where only proxy indicator data are available. My research has addressed these challenges for various indicators via the conceptualization, development, and validation of context-specific Bayesian models. Key components of these models are hierarchical submodels to allow for "sharing" of information across populations to inform the estimates and projections for data-sparse populations, and the parametrization of biases and measurement error variances to account for data quality issues. I collaborate with various United Nations agencies to make available improved estimation methods and resulting estimates to diverse international audiences.