I’m a data-driven researcher focused on using quantitative methods to answer policy-relevant questions. My experience spans Python-based modeling, econometrics, and analytics using large government and administrative datasets, with applications in immigration, labor markets, and broader public policy. I enjoy collaborating across disciplines and building tools that help others make better, evidence-based decisions.
A curated collection of the work I'm most proud of.
Agent-based microsimulation of the employment-based immigration queue, synthesizing 12 years of USCIS petition-level microdata with IPUMS demographic sampling to reconstruct ~5.7M individual applicant state trajectories.