I am a Predoctoral Fellow at Opportunity Insights at Harvard University. My work sits at the intersection of econometrics and machine learning, with a focus on how flexible predictive tools can be incorporated into credible causal research designs.

Much of my research has involved building measurements from noisy or high-dimensional data and studying how those measurements affect inference. In one project using ACS/IPUMS data, I examined how survey misclassification can bias estimates of same-sex parenthood and developed a reconstruction strategy to address contamination in the sample. In other work, I trained decision trees on control-group data to generate out-of-sample predictions of baseline skill in order to study heterogeneity in the effects of generative AI. I have also implemented causal forests to examine disparities in mortgage approval decisions and used embedding-based representations to quantify variation in creative output.

I graduated from Brown University with degrees in Applied Mathematics–Economics and Computer Science (GPA 4.00). Before returning full-time to research, I worked at Boston Consulting Group. I plan to pursue a Ph.D. in Economics.