I received my PhD in Biostatistics in August 2025 from The Ohio State University. My research interests include topics in causal inference, such as the use of matching, weighting, and regression methods in complex and multilevel observational studies, especially in designs with repeated measures. I also bring expertise in randomized clinical trials and aim to advance causal inference thinking in regulatory science and clinical research.

My dissertation, Estimating Causal Effects from Observational Data in Quasi-Experimental Designs, focused on modern problems in quasi-experimental causal inference, especially in difference-in-differences (DiD) designs employed in healthcare utilization studies. This includes addressing the controversial practice of matching on pre-intervention outcomes, and issues of structural zeros in DiD designs. I have also worked on formalizing limitations of retrospective survey data for causal inference, as we have many data constraints (e.g., omission of confounders) that arise from its secondary usage for causal questions, and sensitivity analysis that accounts for variation from survey sample design. Overall, this work not only clarifies some misconceptions in causal analysis with quasi-experiments but also provides powerful tools for practitioners to address common problems in health policy evaluation.

Currently, I am a Biostatistician at Medpace working on the design and analysis of clinical trials. At Medpace, I lead statistical and programming activities for multiple clinical studies, including developing Analysis Plans, selecting appropriate methodologies, programming analyses, and preparing statistical methods for clinical reports. I also support clinical development planning and collaborate with data management and medical writing teams.