Computer Science at the University of Toronto.
Building production systems at Bid Engine.
I like software that earns its complexity.
I am a Computer Science student at the University of Toronto with a Mathematics minor. I build across backend infrastructure, developer tooling, applied machine learning, and product interfaces.
At Bid Engine, I work on production systems for construction tender estimation. My work spans document processing, LLM agents, Java services, event-driven workflows, and cloud infrastructure.
I care about readable architecture, useful safeguards, and products that make difficult work easier.
A lightweight observability platform with a Rust log agent, Go ingestion API, durable storage, search, and production-style reliability controls.
Includes API-key authentication, request validation, cursor pagination, dead-letter handling, dropped-log counters, health checks, migrations, and CI-tested service boundaries.
Rust / Go / PostgreSQL / Docker / Kafka / React
A paper-trading and backtesting workspace with configurable strategies, risk controls, diagnostics, equity charts, and execution history.
Built with modular strategy interfaces, broker validation, paper-execution safeguards, CSV reporting, local-first controls, and automated tests.
Python / FastAPI / React / Vite / SQLite / pytest
A production workflow that extracts construction documents, calls trusted tools, matches suppliers, and routes uncertain results to human review.
The system combines MCP tool use, structured extraction, retryable agent state, confidence gates, event-driven services, and Azure deployment.
Python / Java / Spring Boot / LangGraph / Kafka / Azure AKS
A collaborative machine-learning project for comparing forecasting models through real-time backtesting and portfolio simulation.
The application joined a React interface, Flask APIs, MongoDB storage, live market ingestion, and TensorFlow and scikit-learn models.
React / Flask / MongoDB / TensorFlow / scikit-learn