I’m a mathematics researcher and data scientist passionate about using data-driven and mathematical approaches to understand complex systems and support meaningful, real-world impact.
💡 My background is in theoretical and applied mathematics (algebraic combinatorics, graph theory, modeling), and I’ve transitioned toward machine learning, data visualization, and statistical modeling.
🔬 Recently, I’ve worked on projects such as:
- Modeling housing affordability and redevelopment in Vancouver using clustering and geospatial analytics
- Analyzing community trends on Reddit with NLP and topic modeling (BERTopic, LDA)
- Training a convolutional neural network to predict the release year of music albums from their cover art
🌱 I’m currently exploring representation learning, graph neural networks, and interpretable ML—especially how to make models more reliable, steerable, and mathematically grounded.
🤝 I’m open to collaborations on research or applied projects related to equity, sustainability, and public good, including education, housing, and data ethics.
📫 Reach me at shelburneethan@gmail.com or LinkedIn
⚡ Fun fact: I once performed an escape from a straitjacket while riding a unicycle.