I'm a data science student at the University of Toronto, in the Data Science Specialist, Statistics Major, and Computer Science Minor. My work spans LLM applications, information retrieval, machine learning, and statistical modelling.
I want to work on AI in practice: taking existing models, such as large language models, image models, and classic machine learning, and turning them into working applications that get real tasks done, with data science deciding what is worth building and turning the results into decisions.
Since Sep. 2026 I've been on a U of T research project, supervised by Prof. Nick Koudas (Department of Computer Science), testing whether word-association statistics can help AI search (dense retrieval) cut results that miss a query's topic and find passages that answer it without sharing its words. So far I've built the baselines and search indexes the experiments will use.
- My Second Brain: an AI knowledge app that runs locally and turns text, documents, images, and web pages into a wiki for Obsidian that links each page to its sources, with hybrid information retrieval (keyword and vector search) for answers that cite their sources.
- Global CO₂ Emissions Analysis: a data science project identifying which GDP, energy, forest, and urbanization indicators best predict per-capita CO₂ emissions in 196 countries; an XGBoost model reached a test R² of 0.722. Explore the report.
- LLM Source Classification: a team project identifying whether ChatGPT, Claude, or Gemini wrote a response. I cleaned the survey data and built the feature matrix.
- Meal Master: a Java recipe app built with a team. I implemented the core of the user and recipe data-access objects and took responsibility for the Shopping List use case.