Experience

Team Lead of Team 33. Led the team to win 1st place with a $15,000 prize among 430 competitors across 90+ teams, including PhD, Masters, and Undergraduate students.

Engineered a data-intensive, interpretable AI Anti-Money Laundering (AML) system with machine learning models across individual and small business entities, using customer-level features across multiple transaction types in largely unlabelled financial data.

Designed a source-compliant AML knowledge library (FINTRAC, FINCEN, FLSC, etc) mapping regulatory red flags to data features via AML patterns, enabling SHAP-generated, LLM-enhanced explanations with auditability and factual consistency.

Built a full-stack Next.js web application integrating detection models, explainability, and the AML knowledge library into an investigator-facing workflow platform.

Project Manager and Architect for Agentiiv's MCP Gateway, a production-grade orchestration layer enabling secure AI agent to Model Context Protocol (MCP) server communication.

Defined system scope, architecture requirements, and delivery milestones while coordinating development using Agile Scrum and Jira. Designed and deployed a containerized AWS-hosted gateway integrating 5 MCP servers (134 tools total), including PostgreSQL, Slack, and Google Workspace via a unified MCP protocol.

Implemented core backend infrastructure including deterministic routing, JWT SSO authentication, RBAC access control, centralized logging (PostgreSQL), and rate limiting.

Built platform reliability and observability systems including Prometheus and Grafana monitoring, plus a React dashboard for real-time visibility into request traffic, server usage, system health, and failures; enabled multi-agent workflow execution through gateway-mediated tool access.

Developed AuditMate, an AI web and mobile platform to automate energy auditing for BuildingAssets as part of a UTMIST industry collaboration. Analyzed and preprocessed auditing data, built backend APIs using FastAPI, and integrated the OpenRouter API with Google Gemini agents for computer vision-based fixture identification, manual retrieval, and energy improvement recommendations.

Full-stack developer and organizer of GenAI Genesis 2026, Canada's largest AI hackathon with 2000+ applicants, 800+ hackers (30% increase from 2025), 250+ projects, and 90+ judges. Built and maintained the website, as well as participant and judging platforms, which supported 10,000+ uses during the hackathon.

Designed and implemented secure REST APIs with Zod validation, database schemas, role-based access control, and user interfaces, as well as CI/CD pipelines with GitHub Actions and Vercel for scalable deployment. Collaborated with cross-functional teams, delivering a seamless hackathon experience.

Led the development of the SceneClarity ML project, a modular framework for estimating scene-level reliability in autonomous vehicle perception, and managed the project using Jira.

Analyzed and augmented a dataset of 1M+ images, developed object detection and classification models, and analyzed detection confidence for perception reliability, while achieving 95%+ accuracy on image classification tasks.

Delivered Dockerized REST APIs and a React web application. Presented the project at conferences and events including CUCAI 2026 and EigenAI 2025.

Built integrated bioinformatics and machine learning pipelines for the Functional Gene Expression project, enabling simultaneous benchmarking of 15+ models and achieving 3× faster analysis.

Analyzed multi-dataset gene expression data to identify disease biomarkers and predict autoimmune diseases, trained linear, ensemble, gradient boosting, and SVM classifiers, and applied SHAP/LIME for interpretability.

Implemented Airflow + dbt ETL pipelines to transform and store GEOparse data in a Supabase PostgreSQL database.

Official contributor and modder of the starblast.io game. Developed the official "Capture the Flag" mod, which has been played over 2 million times, improving performance, balance, and engagement.

Developed multiple mods using the Starblast.io API, implementing real-time game logic in JavaScript with WebSockets, and designing custom 3D ships using CoffeeScript for Three.js rendering.

Mentored and led FTC teams 16488 and 22101. Designed and led the development of PID-based motion control and finite state machine architectures for autonomous robot operation.

Implemented real-time object detection using TensorFlow models integrated with OpenCV for vision-based decision making. Developed high-fidelity robotic simulations directly interfaced with the FTC SDK to validate control algorithms and model complex robot behaviors.

Won 2nd place at the Ontario Provincial Championships, as well as Innovate and Design Awards.