Project Portfolio

In this project, I designed and developed an AI-driven decision support tool to automate personalized college advising and improve the efficiency and consistency of recommendations. Built an end-to-end workflow that translates user preferences into a structured scoring model, integrates live tabular data sources, and generates ranked institutional recommendations using automated logic and AI reasoning. Focused on building a scalable, explainable system that mirrors real-world consulting problem-solving by combining data integration, decision frameworks, and process automation to deliver actionable insights.

Project Files

In this project, I created an applied data analysis study conducted in Excel that examines the relationship between real income per capita and health insurance coverage at the U.S. county level. The project involved cleaning and organizing Census data, constructing tables to summarize key variables, and computing descriptive statistics such as means, standard deviations, correlations, and more to identify overall patterns in the data. I created frequency distributions, charts, and graphs to visually present trends across counties, and performed confidence interval analysis for both means and variances to measure uncertainty and consistency in health insurance coverage rates. In addition, I conducted hypothesis testing using t-tests and chi-square-based variance tests to evaluate statistical significance. The results demonstrate a positive relationship between income and insurance coverage, with Democratic counties showing higher average coverage and lower variability, highlighting how income levels and policy environments are associated with health insurance outcomes.

In this project, two colleagues and I researched issues with satisfaction in the current PAYGO system and worked to identify a policy solution. We concluded that partial privatization of Social Security offered the strongest path forward. With higher market returns and reduced fiscal pressure, we argued that this approach could improve retirement planning outcomes and contribute to long-term economic success.

In this project, we aimed to measure projection bias in an individual's risk utility by comparing self-assessment survey responses with decisions made in a risk-related setting involving non-monetary prizes. We developed a proposal outlining what we would study, how we would conduct the research, and why the topic mattered. I believe this project demonstrated high-level thinking about real-world applications and how behavioral insights can produce meaningful economic understanding and change.

In this project, a few colleagues and I took an entrepreneurship class in high school and created a complete business model that we presented to the school board in hopes of receiving funding. Although we did not win the competition, it was still an engaging and meaningful experience. The project challenged me to think more innovatively, strengthened my interest in building new ideas, and reinforced the entrepreneurial mindset I hope to carry into my future work.