Research and projects

Producer prices are supposed to lead consumer prices, but by how long and how reliably? This paper examines the relationship between the Producer Price Index and the Consumer Price Index using U.S. economic data from 1947 to 2025, then checks whether it holds up across the periods where it matters most.

• Built lagged regression models across 3–12 month windows to isolate pass-through timing
• Applied Ridge regression and cross-validation to address multicollinearity between overlapping lags
• Reached an R² of 0.97–0.98, explaining roughly 97% of CPI variance
• Evaluated predictive performance across major economic periods, including 2008 and the COVID-19 inflation spike

A chatbot for contact lens manufacturing operations that lets plant supervisors, maintenance engineers, and quality teams query production data in plain English instead of waiting on a data team.

• Designed a star schema across 12 tables in a Databricks lakehouse, modeling production events and error incidents against dimensions for plants, machines, shifts, products, and error types
• Built an intelligence layer combining clustering to group recurring errors by pattern, z-score anomaly detection to flag deviations in temperature, yield, and cycle time, and correlation analysis to surface co-occurring errors
• Configured a Genie AI interface that resolves natural-language questions into SQL across the full schema and returns results with visualizations
• Stress-tested the system at 100× dataset scale, identifying how statistical assumptions in the anomaly layer degraded accuracy on complex root-cause queries
• Presented the architecture and a live demo to company leadership

An interpretable text classifier built from scratch, then benchmarked against a standard machine learning baseline to see how far transparent, hand-engineered features can get you.

• Split 3,309 labeled sentences into 2,647 training and 662 test examples
• Engineered word-frequency, sentence-length, and punctuation features using log-odds band analysis
• Scored 69.18% with the rule-based classifier against a 70.24% TF-IDF and kNN benchmark

A complete real-time game written in Python and Pygame, presented as a final project and live demonstration at Tufts.

• Implemented a game loop handling input, player movement, enemy waves, and rendering
• Used object-oriented architecture to manage players, enemies, and game state

A university data science program covering data visualization in Python and Jupyter Notebooks, statistical modeling, and predictive analytics.