I study Statistics at the University of British Columbia and use this site to keep a clear record of the projects I can discuss in interviews.

My strongest foundation comes from hands-on ML coursework and data analysis: cleaning data, building features, choosing validation splits, comparing baselines, checking model behavior, and explaining limitations.

I wrote my CPSC 330 machine-learning work and CPSC 221 data-structure labs myself. In newer prototypes and competition projects, I often use AI to speed up implementation, while I set the problem, choose experiments, review outputs, and decide what to change next.