Area of research
Statistics and Probability · Molecular Biology
Research interest
Research interests include Statistical Methods and Inference, Advanced Causal Inference Techniques, Statistical Methods and Bayesian Inference, and Statistical Methods in Clinical Trials.
Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study
Causes and consequences of child growth faltering in low-resource settings
Early-childhood linear growth faltering in low- and middle-income countries
Child wasting and concurrent stunting in low- and middle-income countries
Causal Inference for Social Network Data
Nonparametric efficient causal mediation with intermediate confounders
Efficient nonparametric inference on the effects of stochastic interventions under two‐phase sampling, with applications to vaccine efficacy trials
Doubly robust nonparametric inference on the average treatment effect
Robust Estimation of Encouragement Design Intervention Effects Transported Across Sites
Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring
Entering the Era of Data Science: Targeted Learning and the Integration of Statistics and Computational Data Analysis