Hello! I am a Ph.D. candidate in the Department of Biostatistics at University of Michigan advised by Dr. Walter Dempsey and Dr. Zhenke Wu. In Fall 2023, I will be a Postdoctoral Research Fellow in the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge with Dr. Qingyuan Zhao.
My doctoral research focuses on developing robust and efficient methodologies to evaluate causal excursion effects in sequential, adaptive intervention experiments. My work leverages new experimental approaches, such as micro-randomized trials (MRTs), as well as causal inference and supervised learning methods. The general theme of my research is to advance a more relevant, valid, and precise assessment of digital health intervention effects in the new context of time-varying treatments and repeated outcomes with clustering, interference, and high-dimensional nuisance parameters.
Causal Inference; Causal Excursion Effect; Just-In-Time Adaptive Interventions; Micro-randomized Trials; Mobile Health; Time-varying Moderation Effect; Double robustness.
- 2023.6.29 Submitted Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation on arXiv!
- 2023.6.28 Submitted A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation on arXiv!
- 2023.6.15 Successfully defended my PhD dissertation (with a tweet)!
- 2023.5.23 Presented my work in ACIC 2023 at Austin!
- 2023.3.22 Presented my work in ENAR 2023 at Nashville!
- 2022.12.14 I have been selected as a recipient of a travel award for the 14th International Conference on Health Policy Statistics (ICHPS)!
- 2022.11.20 Submitted Estimating Time-Varying Direct and Indirect Causal Excursion Effects with Longitudinal Binary Outcomes on arXiv!
- 2022.10.05 My first paper Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity is published by Biometrika!