Abstract: Rohit Bhattacharya's (Johns Hopkins University, Expected 2021) research focuses on the development of causal methods that correct for understudied but ubiquitous sources of bias that arise during the course of data analyses, including data dependence, non-ignorable missingness, and model misspecification, in the study of infectious diseases. Rohit approaches these problems by developing novel graphical modeling techniques that can detect and correct for such sources of bias while providing the investigator with clear and interpretable representations of the underlying data dependence or missingness process. In dealing with model misspecification, Rohit has recently developed algorithms that yield doubly robust and efficient semi-parametric estimators for a wide class of causal graphical models, despite the presence of unmeasured confounders. In other work, Rohit has performed several investigations in oncology applications.