I am a biostatistician specializing in causal inference for quasi-experimental designs, with a research focus on matching, weighting, and regression methods for complex, multilevel observational studies, particularly in settings with repeated measures. My work aims to resolve open methodological problems in difference-in-differences (DiD) designs and to advance causal inference thinking in regulatory science, clinical research, and real-world evidence generation.

I received my PhD in Biostatistics in August 2025 from The Ohio State University. My dissertation, Estimating Causal Effects from Observational Data in Quasi-Experimental Designs, addressed several open problems in DiD methodology for healthcare utilization studies, including the controversial practice of matching on pre-intervention outcomes, structural zeros in DiD designs, and limitations of retrospective survey data for causal inference under common constraints such as omission of confounders. This work was recognized with the American Statistical Association’s 2026 GSS/SSS/SRMS Student Paper Award.

I am currently a Biostatistician at Medpace, where I lead statistical and programming activities across multiple clinical trials, including developing analysis plans, selecting methodology, and preparing statistical reporting for regulatory submission. This applied clinical trials experience directly informs my methods research, grounding my work in the practical constraints and open questions that arise in real-world regulatory and clinical settings.