Rye, New York, United States
Early technical contributor on core engineering team; enabling LLMs to understand and use tabular data.
Consumer Product Group; Beginning Summer 2026
Supporting CS 577: Introduction to Algorithms under Professors Marc Renault and Dieter van Melkebeek. ❖ Reduced quiz grading turnaround time by 59% for 700+ students by developing a Python CLI tool that parses Canvas quiz exports and generates student-specific PDF reports. ❖ Provide academic support through 10+ office hours weekly, answering individual questions and guiding students through problem-solving strategies. ❖ Host 14 weekly hour-long study sections for groups of 20+ students, reinforcing concepts such as Greedy Algorithms, Divide and Conquer, Dynamic Programming, Network Flow, and NP-Completeness.
Worked on a UW-Madison research project applying machine learning to optimize donation campaign outcomes for a local food pantry, under Professors Benjamin Afflerbach and Dane Morgan as part of the Wisconsin Undergraduate Research in Data Science group (previously Informatic Skunkworks). ❖ Designed an end-to-end Python ML pipeline using Pandas, Scikit-Learn, and XGBoost that predicts individual donation amounts within ≈ $45 on average, explaining over 60% of donation behavior for a local food pantry. ❖ Achieved 3× feature expansion, growing a raw 5-column donation dataset to 15+ enriched fields (demographics, campaign context, seasonality) through Python web scraping, API integrations, and exploratory analysis. ❖ Trained a Random Forest classifier on 40+ engineered features, correctly flagging 71% of future repeat donors. ❖ Integrated U.S. Census APIs to enrich 7,000+ donor zip codes with median income and distance-to-pantry metrics. ❖ Occasionally volunteering at The River Food Pantry as part of my undergraduate research. Project Name: Optimizing Rate of Return from Donation Campaigns using ML Modeling and Feature Analysis