I am most interested in developing semiparametric efficiency theory and nonparametric statistical methods in causal inference and survival analysis. Much of my research concerns obtaining valid statistical inference when using machine learning and other data-adaptive nuisance estimators. Recently, I have worked on several problems related to causal inference with continuous exposures, including debiased inference for causal dose-response curves (JRSS-B, 2024), testing causal null hypotheses with continuous exposures (JASA, 2021), and inference for monotone causal dose-response curves (JRSS-B, 2020). I am grateful to be funded by the National Science Foundation Division of Mathematical Sciences award number 2113171 for my research focusing on causal inference with continuous exposures. I have also worked at the intersection of targeted learning and shape-constrained estimation, including developing general theory for nonparametric inference on monotone functions (Ann. Stat., 2020) and inference on counterfactual survival curves (JASA, 2024).
I am grateful to be funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development awards R01HD110462 and R01HD106108 for collaborative research with scientists in the UMass Amherst Department of Biostatistics and Epidemiology on causal pathways of cardiovascular disease following pregnancy complications and mitochondrial biomarkers of male reproductive health. I have also worked with researchers and clinicians in the Pediatric IDEAS research group at the Children's Hospital of Philadelphia to develop and apply statistical methods to address questions relating to the prevention and treatment of pediatric infectious diseases and with researchers at Fred Hutch Cancer Center on projects related to vaccine and oncological applications, examples of which can be found here, here, and here.