• Developed a deep learning model (Tensorflow Convolution Neural Network) to identify and extract dates from check images with 98% accuracy to enable faster processing of remote deposited checks• Created a real-time GBM fraud model that prevented 70% fraud losses in person to person transactions.• Developed a model refit pipeline to automatically retrain models on new fraud trends• Created an R Shiny app to automate monitoring of deployed fraud models to identify new fraud trends and monitor model performance in real-time

National Science Foundation (NSF) Record Linkage Project• Used Fellegi-Sunter and Random Forest models to link administrative records of 206,586 students from nine state universities to 65,310 individuals in the Survey of Earned Doctorates from NSF, creating a new linked dataset to answer research questions about federal science fundingNational Assessment of Educational Progress (NAEP) R Package Project• Created an R package optimized for large datasets and low memory to calculate sampling and imputation variance (using JRR and Taylor series), correlation statistics, and gap analysis for NAEP data• Developed an R package to calculate weighted polyserial and polychoric correlations U.S. Census Bureau Small Business Administration Project• Analyzed measures of female and minority earnings and labor force participation in small businesses • Used visualization techniques including ggplot and shiny to present results to stakeholdersBig Data for Federal Agencies (BDFA) Project• Created workbook assignments in IPython that walk students through examples of applications of statistical machine learning techniques and text analyticsTeacher and Leadership Evaluation Systems (TLES) Study • Wrote software in R to assign 1,500 videos of teachers to raters, implementing a complicated survey design that accounted for unbalanced data and allowed for the validation of scores by assigning select videos to multiple raters• Programmed STATA to run a hierarchical mixed model to determine impacts of TLES on student achievement and teacher mobility

• Taught fundamentals of Python, HTML, CSS & JavaScript to computer science students during lab hours• Supervised student discussions on weekly coding assignments during open lab hours

• Enforced University and Residential Life rules and regulations in a residence hall floor of ethnically diverse undergraduate students resulting in a safe, orderly and enjoyable living environment• Organized programs at the floor, hall and campus level in response to residents’ interests and needs

• Developed multi-player client server games using Java to analyze the assumptions of rational choice theory and gained a deeper understanding of behavioral economics by exploring the data collected