My primary research interests can be broadly summarized as developing and applying novel statistical methods to studies of HIV/AIDS. Since 2006, I have been the lead statistician for the Caribbean, Central and South American network (CCASAnet) of the International epidemiologic Databases to Evaluate AIDS (IeDEA). I am currently the Director of the Data Science Core (DSC) of the Tennessee Center for AIDS Research (TN-CFAR); I served as the head statistician for the former Vanderbilt-Meharry CFAR from 2006-2013. I have also supervised biostatistical support for the Vanderbilt Institute for Global Health (VIGH) since 2006. In these contexts I have collaborated with many researchers on a wide variety of HIV/AIDS research topics. My statistical methods research has been motivated by problems I have encountered in my collaborative work, with a particular emphasis on causal inference and methods for epidemiological data. As a graduate student at the University of Washington, I developed causal inference methods important to HIV vaccine trials. Upon coming to Vanderbilt, I have continued to develop and apply causal inference methods, and have branched into other areas including approaches for analyzing ordered categorical data, and measurement error methods. My statistical methods research has led to external funding. I currently serve as PI of two R01s developing statistical methods applicable to HIV/AIDS: One grant studies approaches to leverage the order information in ordinal data to increase statistical power, the other grant investigates methods and designs to use audit information to improve estimation in a larger observational cohort. I am also PI of a methods grant from the Patient Centered Outcomes Research Institute that is developing statistical techniques to improve data quality in electronic health record data. I serve as the director for the Data Science Core of the TN-CFAR.