Aspiring Data Analyst striving to use my technical skills to achieve social justice with data.

Education

• M.S. in Data Analytics and Computational Social Science from University of Massachusetts, Amherst, MA (May 2025)
• B.A. in Data Science and Women’s & Gender Studies from Wheaton College, Norton, MA (May 2024)

Work Experience

Tutor-Consultant/Classroom Assistant at the University of Massachusetts, Amherst, MA (Sep. 2024 - present)

• Guide graduate students in DACSS 601: Data Science Fundamentals and DACSS 756: Machine Learning for Social Scientists in transforming, visualizing, and building models with data in R.

Break Through Tech AI Fellow at the Massachusetts Institute of Technology, Cambridge, MA (May 2023 - Apr. 2024)

• Built a restaurant recommendation system from 1,200 user ratings using matrix factorization and collaborative filtering with a team, guided by a mentor from Dropbox.
• Constructed a convolutional neural network for plant specimen identification for the New York Botanical Gardens, placing in 12th out of 76 teams with an image classification accuracy of 98%.

Teaching Assistant at Wheaton College, Norton, MA (Sep. 2022 - Apr. 2024)

• Assessed homework and provided actionable feedback for students in MATH 151: Introduction to Data Science, MATH 241: Theory of Probability, and MATH 342: Mathematical Statistics.
• Assisted students in COMP 118: Object-Oriented Programming with their C++ lab assignments.

Learning & Evaluation Fellow at Project Hope, Roxbury, MA (May 2022 - Aug. 2022)

• Collected, cleaned, and analyzed data in Microsoft Excel and Efforts To Outcomes (ETO) to create summaries and reports that were shared within the organization and with external funders.

Skills

• R
• Python
• SQL
• Excel
• C++

Projects

Recreating the Oscar Awards Database (December 2024)

• Completed for DACSS 690A: Data Engineering taught by Dr. Tyler Horan
• Designed an ETL pipeline to extract data from all 96 Academy Awards ceremonies from the Oscars website, transform it into a tabular format, and load it into a Flask application to present statistics and visualizations.
• Oscars-Data Repository

Performing Sentiment Analysis on Vampire Movie Reviews (December 2024)

• Completed for DACSS 758: Text as Data taught by Dr. Rosemary Pang
• Built Naive Bayes, Support Vector Machine, and Random Forest models to predict whether reviews of vampire movies from Rotten Tomatoes were positive or negative and determine which words were most important to making those predictions.
• Vampire-Reviews Repository

Developing a Smart Restaurant Recommendation System for Dropbox (December 2023)

• Completed for the Break Through Tech AI Program
• Used Matrix Factorization to predict which restaurants users may like based off of their past ratings of other restaurants.
• Dropbox-Restaurants Repository

Using Machine Learning to Analyze the Highest-Rated Films on IMDb (May 2023)

• Completed for MATH 398: Machine Learning taught by Dr. Michael Kahn
• Applied Multiple Linear Regression, Lasso Regression, Ridge Regression, Best-Subset Selection, and Regression Trees to predict ratings of films on IMDb and determine which predictors were most important to making those predictions.
• IMDb-Ratings Repository