💻 3rd-year Computer Science & Economics (Honours Co-op) student at McMaster University 🤖 Interested in AI agent systems, backend infrastructure, and full-stack development 🛠 Open-source contributor on Drasil, a McMaster research project 🚀 I build things that actually ship — not just prototypes
Full-stack OCR pipeline (Next.js frontend on Vercel, FastAPI backend on Modal) that converts handwritten notes into editable LaTeX in real time, streaming results via Server-Sent Events. Confidence-gated fallback OCR system plus an LLM correction pass, benchmarked at 71.4% mean character accuracy on real photographed handwritten notes. Full CI test suite on every push.
📁 MathScan · Live: math-scan-lake.vercel.app
Real-time observability API detecting AI agent hallucinations, tool-call loops, and reasoning drift via a per-span composite anomaly scorer combining linguistic, latency, and retry-pattern signals. Three-tier automated remediation (WARN / PAUSE-with-human-gate / ABORT), backed by 31 unit tests. Submitted to the Splunk Agentic Ops Hackathon ($20K prize pool, 1,300+ participants).
📁 AgentSentinel · 🎥 Demo
Full-stack AI pipeline built in 24 hours — React/TypeScript frontend, FastAPI backend, real-time audio classification using MFCC, spectral centroid, and zero-crossing rate features. Placed 3rd of 20+ teams (Health & Aging track) at MacHacks 2026.
📁 DysphagiaSense
Merged 10 pull requests into a multi-package Haskell codebase spanning printer backends, code generators (C#, Python, Java), and requirements-engineering domain logic — used across 10+ downstream case studies. McMaster research project supervised by Dr. Jacques Carette and Dr. Spencer Smith.
📁 Drasil
Languages Python • TypeScript • JavaScript • Java • C • SQL • Go • Haskell • HTML/CSS
Frameworks & Libraries FastAPI • React • Next.js • Node.js • REST APIs • NumPy • Pandas • Librosa
Cloud & DevOps AWS • Docker • Modal (serverless) • CI/CD (GitHub Actions) • PostgreSQL • MySQL • Git • Linux
Concepts OOP • Distributed Systems • Unit Testing • Agile • API Design
- Shipping full-stack systems end-to-end — idea to deployment, not just demos
- Debugging real production issues (deployment failures, root-cause fixes) rather than working around them
- AI/agent infrastructure: observability, evaluation, and reliability
- Writing tested, maintainable code with real CI pipelines