The rapid growth of big data, machine learning, and artificial intelligence (AI) has transformed how economists, businesses, and policymakers address complex problems. My research lies at the intersection of applied econometrics, machine learning, AI, health economics, labor economics, and business analytics. By integrating computational methods with economic theory, I develop interpretable, data-driven models that generate rigorous evidence to inform business strategy and public policy. My completed research demonstrates how modern analytical methods can improve understanding of health, labor, and economic outcomes while maintaining transparency and interpretability. One line of research applies double machine learning and causal inference to evaluate the health risks of vaping relative to smoking, providing evidence relevant to tobacco regulation and harm-reduction policy (revise and resubmit at *PLOS ONE*). Another develops interpretable machine learning models to predict chronic health conditions, including diabetes and depression, balancing predictive performance with explainability to support healthcare decision-making (published in *Healthcare Analytics*). I have also combined econometric and machine learning methods to examine cooperative membership and farmer welfare in South Africa, contributing both methodological innovations and policy insights (published in *Scientific African*). In addition, my work has examined student belonging, educational outcomes, and labor-market experiences among underrepresented populations, highlighting how institutional support influences performance and long-term mobility. My current and future research extends these themes by applying causal inference and machine learning to labor economics, health economics, education analytics, and management decision science. A major focus examines how AI is reshaping work, occupational tasks, and wage dynamics in the U.S. labor market using large-scale administrative and survey data. Related projects investigate workers' behavioral responses to automation risk and the implications of AI adoption for labor-market inequality and firm strategy. I am also expanding my work in education analytics, including student performance, sorting, and AI-assisted learning, as well as business applications of AI in pricing, resource allocation, and strategic decision-making. I currently have nine working papers in various stages of development, several of which are nearing journal submission.