Projects

This section is where theory meets practice. Each project here represents a different slice of what I bring to data work: collecting messy data from the web, building and comparing machine learning models, applying cutting-edge NLP techniques, creating interactive tools to make my partner happy, and making complex methods accessible to others.

Some of these grew out of pure curiosity (what patterns emerge in song lyrics? Can we detect “eras”?), others from practical needs (how do I make my teaching materials easily searchable for my students?), and a few from different research challenges (what is the best classifier for a job? how good are LLMs actually? is political polarization really so hot?). Together, they show how I approach problems: I start with a question, pick the right tools and data for the job, and don’t stop until I’ve found an answer worth sharing.

You’ll see a mix of techniques here: classic machine learning, modern transformer models, web scraping, interactive dashboards, and RAG applications. But the thread that runs through all of them is the same: how can I turn raw data into understandable insights, into something people can actually understand and use.

What we can learn about runners from Strava

I scraped 1,000+ Strava activities from the 2025 Boston Marathon to analyze what gear runners wear and whether it correlates with performance. Using Selenium to navigate dynamic webpages and wrestling with messy user-generated data, I dove into the world of running watches shoes. Spoiler alert: Garmin dominates the watch market, Nike rules the shoe game among faster runners, and despite what the marketing says, there’s no statistically significant evidence that shoe brand affects how hard you work during a marathon. READ MORE