Research Assistant
PythonRSQLC++StataLLMsMATLABFortran
- Conducting quantitative economics and finance research using Python and R, analyzing data and applying statistical modeling and economic modeling to large-scale financial, patent, and licensing datasets.
- Building a large-scale database and data pipelines using SQL, Python, and local LLMs, optimizing extraction of firms, technologies, and licensing terms under compute and latency constraints.
Platform Engineer Junior
Apache KafkaConfluent CloudApache Flink SQLPowerBIBackstageNext.jsReactNode.jsAzure OpenAIPostgreSQLGCPGoogle Cloud SQLGoogle Cloud Storage (GCS)TerraformGitHub ActionsGitHubBitBucketJira
- Data Platform Engineer Intern on the Event-Driven Services team, enhancing the Event Exchange self-serve portal used by 150+ bank-wide teams to improve Kafka resource discovery, developer workflows, and data access.
- Built Catalog 2.0, a reporting and governance platform for a Kafka ecosystem processing 6B+ messages and 1TB+ data monthly; conducted extensive data analysis, automated data pipelines, and developed interactive Power BI reports with platform APIs, PostgreSQL, and GCP, improving data accuracy by 12% and reducing report generation time by 60×.
- Developed reusable full-stack Backstage plugins for event-driven developer workflows using React and Node.js, and established a scalable plugin architecture enabling 5+ teams to build self-service capabilities through automated GitHub Actions and Terraform workflows.
- Built an AI-powered financial crime detection PoC using Kafka, Flink SQL, Confluent Cloud, and Azure OpenAI for low-latency stream processing, real-time anomaly detection, LLM-based alert enrichment, and AI-assisted case triage; designed a scalable architecture for future real-time AI banking applications and presented the solution to CTO, EVP, SVP, and VP-level leaders as well as engineering teams across Scotiabank.
Team Lead
Scotiabank × IMI BIGDataAIHUB arrow_outward IMI Big Data & AI Competition 2025-2026 December 2025 – April 2026 emoji_events 2025-2026 IMI Big Data & AI Competition, First place with a $15,000 prize arrow_outward
PythonPandasNumPyscikit-learnimbalanced-learnSHAPLIMENext.jsReactJupyter NotebookAnacondaGitHub
- Led Team 33 to develop an interpretable AI Anti-Money Laundering (AML) platform combining machine learning (ML), explainable AI, and regulatory intelligence, and won 1st Place with a $15,000 prize among 430 competitors across 90+ teams, including PhD, Masters, and Undergraduate students.
- Designed and developed AML anomaly detection pipelines using Python, scikit-learn, and imbalanced-learn across 61,000+ transactions; expanded and optimized the initial 2-model framework into a 16-model framework with 14 transaction-specific models across 7 transaction types, identifying 6.5× more AML critical risk cases.
- Built explainable AML decision workflows combining SHAP, LIME, and LLM-generated explanations with regulatory knowledge sources including FINTRAC, FINCEN, and FLSC, improving transparency, auditability, and investigator confidence.
- Developed a full-stack investigation platform using Next.js and React to integrate anomaly detection results, AI-generated case explanations, and a knowledge library of AML red flags and suspicious activity patterns into a unified system.
Project Lead
MCPFastAPINext.jsReactPostgreSQLAWSSlackGoogle WorkspacePrometheusGrafanaDockerJiraGitHub
- Architected and delivered the MCP Gateway project, a production-grade orchestration layer enabling secure AI agent-to-MCP server communication. Defined system architecture, technical requirements, and Agile delivery milestones using Jira.
- Designed and developed a containerized AWS-hosted gateway using FastAPI and Docker, integrating 5 MCP servers (134 tools) across PostgreSQL, Slack, and Google Workspace; implemented JWT SSO authentication, RBAC, centralized PostgreSQL logging, and rate limiting.
- Built platform observability infrastructure using Prometheus, Grafana, and a custom React dashboard to monitor request traffic, server utilization, system health, and failures, enabling reliable multi-agent workflow execution through centralized tool access.
Software & Machine Learning Engineer
PythonFastAPIOpenRouter APINext.jsReactFlutterAWS EC2GitHub
- Developed AuditMate, an AI-powered web and mobile platform for automating building energy audits at BuildingAssets. Performed data analysis and image preprocessing, and built backend APIs using FastAPI to support audit workflows.
- Integrated OpenRouter API with Google Gemini agents for computer vision-based fixture identification, manual retrieval, and energy improvement recommendations.
- Built Next.js and Flutter frontends, and deployed on AWS EC2, enabling both professional auditors and self-serve clients through automated and guided audit experiences.
Technology Director
GenAI Genesis arrow_outward October 2025 – March 2026
Next.jsReactSupabasePostgreSQLREST APIsZodGitHub ActionsVercelJestFigmaGitHub
- Organizer of GenAI Genesis 2026, Canada's largest AI hackathon, leading technology development for participant and judging platforms supporting 2,000+ applicants, 800+ hackers (30% YoY growth), 250+ projects, and 90+ judges; built and maintained platforms that facilitated 10,000+ interactions throughout the event.
- Engineered scalable full-stack infrastructure using Next.js, React, Supabase, and PostgreSQL, implementing secure REST APIs, Zod validation, database schemas, and role-based access control to support participant workflows and judging operations.
- Built and deployed automated CI/CD delivery pipelines using GitHub Actions and Vercel, improving release reliability and reduced release time by 35% through continuous integration and streamlined deployment workflows while achieving zero-downtime operations throughout the hackathon.
Machine Learning Project Team Lead
UTMIST arrow_outward Aug 2025 – April 2026
PyTorchTensorFlowScikit-learnPandasNumPyDockerREST APIsNext.jsReactGitHubVSCodeJupyter NotebookGoogle ColabJira
- Led the development of the SceneClarity machine learning 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.
Data & Machine Learning Engineer
UT BIOME arrow_outward September 2025 – March 2026
scikit-learnPandasNumPySupabasePostgreSQLAirflowdbtGitHub
- 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.
Game Mod Developer
Starblast.io arrow_outward August 2021 – February 2026
JavaScriptGitGitHubVisual Studio Code
- Official contributor and modder of the starblast.io game. Developed the official "Capture the Flag" mod, which has been played over 2 million times, and optimized the mod to double the frame rate (FPS), 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.
Mentor & Programming Lead
FIRST Tech Challenge (FTC) Teams 16488 & 22101 arrow_outward September 2021 – February 2026 emoji_events Robotics FIRST Tech Challenge (FTC) 2024-2025 Season, FTC Ontario Provincials Design Award 2nd Place | Team 16488 arrow_outwardemoji_events Robotics FIRST Tech Challenge (FTC) 2023-2024 Season, Ontario Provincials 2nd Place, Innovate Award 1st Place | Team 16488 arrow_outward
TensorFlowOpenCVJavaAutodesk Fusion 360OnShapeGitGitHub
- 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, increasing the successful autonomous run rate by 30% through iterative simulation-driven tuning.
- 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.