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.
I was also part of the Virtual AppLication-supported ENvironment To INcrease Exercise (VALENTINE) Study, which aims to evaluate a digital intervention to supplement cardiac rehabilitation for low- and moderate risk patients, integrating a mobile application with physiologic and contextual information from wearables to provide incremental support to patients.
Since September 2023, I’ve been working on projects developing data integration methods for Medallian Randomization and conditional independence testing for Granger Causality. For a complete list of my work, please refer to my Google Scholar profile.