Research Assistant in the Banking and Financial Analysis (BFA) section within the Division of Monetary Affairs.

Created and continue to maintain website that provides learning material to help students improve study skills and have a more successful and enjoyable school experience.

Conduct quantitative and qualitative research and analysis, both academically and independently. Build datasets and models using Python, R, Stata, and Excel; analyze and interpret results; and present valuable insights in written and oral form, often as a team leader. Explained and predicted the success of professional baseball/football teams and players, examining areas such as payroll, attendance, and umpire bias. Predictive models used methods such as Logistic Regression, Naive Bayes, Random Forests (decision trees), and Support Vector Machines (SVM). Analyzed data from the National Longitudinal Survey of Adolescent Health (Add Health) and National Longitudinal Survey of Freshman (NLSF) to determine the factors that explain college success and risky behavior, such as binge drinking, among young adults, and suggest policies to address these areas. Predicted college football results based on analysis of historical data, achieving the highest accuracy in a class of 30 students.

One of 24 student researchers selected for a semester course and one-week field study in Silicon Valley and San Francisco, meeting with senior executives, entrepreneurs, and venture capitalists at 20 technology firms. Managerial-focused course work and visits, highlighting executive, marketing, finance, operations, R&D functions, the growing importance of analytics in business strategies, and understanding success drivers.

Conducted a three-month project for Sweetgreen, a farm-to-table salad chain collecting and analyzing data on measures such as demand, flow time, inventory, and capacity. Met with management and recommended improvements, including resource allocation and expanded use of forecasting and mobile ordering, to better align with strategy.