The rapid expansion of big data, machine learning, and artificial intelligence (AI) has transformed how economists, businesses, and policymakers understand and address complex decision-making problems. My research sits at the intersection of applied econometrics, machine learning, AI, health, labor, and business analytics, with a central emphasis on producing interpretable, rigorous, and policy-relevant insights. By integrating advanced computational methods with economic and management theory, my work addresses pressing challenges in labor markets, healthcare systems, education, and organizational decision-making. My completed research demonstrates how modern analytical tools can deepen understanding of health, labor, and economic outcomes while maintaining transparency and interpretability. One line of work applies double machine learning and causal inference to evaluate the health risks of vaping relative to smoking, generating evidence relevant for tobacco regulation and harm-reduction policy (R&R at PLOS ONE). Another study develops interpretable machine learning models to predict chronic health conditions such as diabetes and depression, balancing predictive accuracy with explainability to support responsible decision-making in healthcare and public policy (R&R at Healthcare Analytics). In related work, I combine econometric and machine learning approaches to examine cooperative membership and farmer welfare in South Africa, contributing both methodological insights and applied policy relevance (R&R at Scientific African). I have also studied student belonging, educational outcomes, and labor-market experiences among underrepresented populations, highlighting how institutional design and support mechanisms shape performance, retention, and long-term mobility. Building on these foundations, my current and future research agenda focuses on applying machine learning, causal inference, and advanced statistical methods to generate actionable insights in labor economics, health economics, education analytics, and business decision science. A major stream of ongoing work examines how AI reshapes work, tasks, and wages in the U.S. labor market. One project uses large-scale microdata and computational causal methods to study how AI alters occupational task composition and wage dynamics, while another investigates worker behavioral responses to automation risk. These projects contribute to debates on the future of work, labor policy, and firm-level strategy. I am also expanding my research in education analytics—examining student performance, sorting, and the role of AI in learning environments—as well as managerial applications of AI, including pricing, resource allocation, and competitive strategy. Currently, I have nine working papers in various stages of development, several of which are nearing journal submission.