🏃‍♂️ Running enthusiast (newly unlocked) • 💻 Software & Data @ UofT • ☕ Powered by curiosity and an unhealthy amount of caffiene 📍 Toronto, Canada | 🎓 Computer & Data Science (Co-op, May 2027)

Recently got into running — turns out it’s great practice for facing problems head-on.
(Also great for debugging… eventually. 🥲)

I’m a Computer & Data Science student at the University of Toronto. I'm always trying to learn something new to help me approach problems with a new lens.

I enjoy:

• Taking ambiguous problems and turning them into simple systems
• Owning projects end-to-end (design → code → deploy → iterate)
• Learning by building (and occasionally breaking things responsibly)

Currently:

• 💼 Full Stack Developer @ Scotiabank (Global Banking & Markets)
• 🛠 Building ReturnFlow, a Rails returns & exchanges platform
• 🏃‍♂️ Running (mostly from bugs, sometimes from my responsibilities)

A self-serve returns portal + merchant dashboard designed like a real commerce platform.

Why I built it:
Because returns are messy, async, stateful, and full of edge cases — aka a great systems problem.

Highlights

• 🏬 Multi-tenant Rails 7 app (store-scoped data model)
• 🔁 Validated state transitions (requested → approved → received → refunded/exchanged)
• 🧾 Append-only event log for traceability & auditability
• ⚙️ Sidekiq + Redis for async jobs (notifications + webhooks)
• 🔐 Webhooks with HMAC signatures + retry/backoff
• 📊 Analytics on return reasons & SKU hotspots

Think Shopify returns, but built in a hackathon-style sprint (with fewer meetings).

📦 Repo: coming soon (currently sprinting 🏃‍♂️)

• Cut manual email triage by 80% by building an LLM-backed classification pipeline (RAG + evaluation + safe fallbacks)
• Reduced multi-team processing timelines from months → days by owning a mission-critical approvals platform end-to-end
• Built a cosine similarity REST service with caching + load testing to validate 10× scaling

• Reduced AWS containerization costs by 40% via multi-stage builds, right-sized compute, and worker consolidation
• Built a geospatial scenario builder UI (React/TS) for ML-driven planning with OpenAI + Ollama
• Reduced Django + PostgreSQL API latency by 35% via profiling, ORM optimization, and indexing

• Saved $12,000 by in-housing an event signup app serving 9,300+ users
• Cut Firebase reads by 30% via caching + query restructuring
• Reduced check-in time by 50% using SendGrid + QR-code automation

• 📈 Real-Time Market Simulation & Prediction Engine
  Built a real-time FX market simulator with PyTorch; sped ingestion 6× using Polars over pandas.

• 🌪 Hurricane Preparedness Aid
  GenAI RAG over emergency documents + real-time tracking maps; Dockerized for reproducible deployment.

Languages
Python • TypeScript/JavaScript • Java • C# • SQL • Ruby (learning)

Frameworks & Tools
Rails • React • Django • Flask • PyTorch • Redis

Infrastructure
AWS • Docker • CI/CD • PostgreSQL

Data / ML
Transformers • RAG systems • Polars • Pandas • NumPy

• Recently started running — turns out consistency beats motivation (same applies to debugging)
• I like systems that are boring in production (the highest compliment)
• Favorite performance metric: “this used to take forever”