• Trained machine learning (ML) models with results presented to client as policy suggestions to improve programs related to the criminal legal system and behavioral health• Developed insights using linear and logistic regression, random effects models, random forest and CART, latent class analysis, gaussian mixture models, difference-in-difference, ARIMA models, statistical analyses• Created final report visualizations about complex longitudinal data in R and Tableau• Implemented data preparation pipelines in R for cleaning and organizing structured and unstructured data

• Integrated internal and external databases using fuzzy matching to produce ESG insights for investment decisions• Partnered with data science team to identify an appropriate problem, then led intern team in 3-day competition to architect and build a prototype solution on AWS (using DynamoDB, Lambda, Athena and Quicksight)• Improved production code to create, access, and secure cloud databases as part of Scrum team using Python, AWS CDK, and Azure DevOps Pipelines. Setup SQL queries to work with both Amazon Aurora and DynamoDB

● Coauthored 2 papers on modeling prostate cancer disease progression with ODEs using MATLAB● Trained support vector machine model to predict patient outcomes, analyzed key parameters using correlation matrix● Refactored code, reducing codebase by 75%, automating reading of new patient data and introduced version control

● Held office hours, answered student questions on Slack, edited course documents for clarity and guided students though working together as an Agile Scrum team to modify a large and messy Java codebase into a new application ● Resolved student issues with Git/GitHub, installing software, and using the command line● Created a 15-minute video to explain common misunderstandings and errors with Gradle