BSc (Honors) Computer Science @ University of Alberta · GPA 3.7 · Expected May 2027
I build things end-to-end — data pipelines, backend APIs, ML models, and the tooling around them. My work spans data engineering (ETL, SQL, reporting automation), applied ML (predictive modelling, computer vision), and systems programming (networking, concurrent servers). I care about shipping things that are correct, documented, and maintainable — not just things that run.
- SQL Server ETL Pipeline — Python ETL + T-SQL stored procedures + Power BI dashboard + automated PowerPoint reporting. Full stack from raw CSV to executive summary, with audit logging throughout.
- F1 Race Pace Predictor — Modular data pipeline over FastF1 telemetry. Regression + classification with GroupKFold CV (MAE ~0.32s, F1 ~0.76). Streamlit dashboard for interactive exploration.
- LabPortal REST API — Flask + PostgreSQL + Docker + GitHub Actions CI. Normalized schema, CRUD with structured error handling, pagination, integration tests, and a developer onboarding guide.
- TestBench CI — Automated regression suite (unit, integration, contract, SQL, Selenium e2e) for a Flask app. Containerized with Docker Compose, artifacts on failure, CI on every PR.
- Vision-Based Object Detection & 2D Pose Estimation — Dual pipeline comparing HOG+SVM vs CNN. Encoder-decoder pose net predicting Gaussian heatmaps for 17 keypoints. Runs on CPU with synthetic data.
- Sentio — Android mood-tracking app in Java/Kotlin with Firebase/Firestore. Offline-first data layer with queued sync, conflict resolution, and role-aware access controls.
- Box Office Breakdown — EDA on 4,800+ films (TMDB). Feature engineering, correlation analysis (budget explains ~50% of revenue variance), five research questions, structured report.
- Deeper Power BI / DAX modelling
- Computer Networks & Distributed Systems (CMPUT 313/481)
- Machine Learning II (CMPUT 467)
