Canopy: AI Course Generation & Mastery Platform
OpenAI Build Week Hackathon
July 2026 
GitHub arrow_outward
GitHub arrow_outward GPT-5.6CodexPythonTypeScriptNext.jsReactFastAPIPydanticSupabasePostgreSQLpgvectorpgmqDockerOpenAIAzure OpenAIAWS BedrockMaterial UIMonaco EditorpytestAddressSanitizerPyTorchscikit-learnNode.jsGoC++
- Canopy turns a source, a research paper, documentation, or personal notes, plus a stated learning goal into a complete course of source-grounded lessons, worked examples, quizzes, and coding labs, tracking per-concept mastery as separate Understand (quiz) and Apply (lab) estimates via Bayesian Knowledge Tracing, with prerequisite-aware review for weak concepts.
- Coding labs run in a Monaco-based editor and are verified before reaching learners through a bounded generate, execute, repair, and re-verify loop: reference solutions run against hidden tests in fresh, network-disabled, non-root sandbox containers, with real tracebacks fed back into regeneration.
- Built as a five-service platform, Next.js web app, FastAPI service, ingestion/generation worker, LLM gateway, and sandbox runner, backed by Supabase for auth, storage, Postgres, and vector search. GPT-5.6 is routed by stakes: flagship Sol for planning and lab repair, faster Luna for high-volume authoring, grading, and the in-lesson helper.
- Built by Javier Huang and Ethan Qiu for the OpenAI Build Week Hackathon; the demo course was generated end to end from Vaswani et al.'s "Attention Is All You Need" (arXiv:1706.03762).
Quant Sandbox
Local-First Quantitative Trading Research Platform
July 2026
PythonFastAPISQLAlchemySQLitePandaspandas-taWebSocketsscikit-learnPyTorchSHAPHydraMLflowOANDA APIAlpaca APIInteractive Brokers APINext.jsReactTypeScriptMaterial UITanStack QueryGitHub
- Designed and built Quant Sandbox, a local-first quantitative research platform for developing, backtesting, and paper-trading systematic strategies (rule-based and ML-driven alpha models) with full decision-level attribution, utilizing a Python/FastAPI engine layer paired with a Next.js/TypeScript research dashboard.
- Architected a shared Event schema (OHLC, indicator state, buy/sell/hold/close signal, and a mandatory human-readable trade rationale, e.g. "RSI(14) 28.4→31.2, crossed above 30 → oversold bounce") so vectorized backtests and live paper-trading sessions emit identical event traces, replayed through one candle chart, indicator pane, decision log, and equity curve for both historical and real-time analysis.
- Built a pandas-vectorized backtesting engine with configurable transaction cost modeling (spread, slippage, commission), persisted per-run event traces and P&L/drawdown stats broken down by month/quarter, and a plugin strategy framework covering trend-following (SMA/EMA crossover), mean-reversion (RSI), and momentum (Bollinger Band breakout) signal logic.
- Implemented two capital allocation schemes for multi-strategy/multi-asset portfolio construction with dynamic leader-takes-capital rotation by trailing performance, and conviction-weighted capital splitting across simultaneously traded instruments, plus versioned, snapshot-safe trading system configs reusable across backtests, portfolios, and live runs.
- Wired live paper-trading execution to OANDA, Alpaca, and Interactive Brokers (plus a synthetic broker for credential-free testing), enforcing bar-by-bar strategy evaluation identical to backtest semantics, with real-time candle aggregation from streamed tick data for sub-minute granularities.
- Built an ML alpha-research pipeline (Hydra-configured, MLflow-tracked) spanning signal generation, regime detection, volatility forecasting, meta-labeling/bet sizing (López de Prado-style), and SHAP-based model diagnostics, with single-run, hyperparameter grid search, and rolling walk-forward validation launchable from the dashboard.
Eyas: AI Security Camera Agent arrow_outward
2026 Hugging Face Build Small Hackathon, Best Agent Award with a $1,000 Prize
June 2026 GitHub arrow_outward emoji_events 2026 Hugging Face Build Small Hackathon, Best Agent Award with a $1,000 prize arrow_outward
GitHub arrow_outward emoji_events 2026 Hugging Face Build Small Hackathon, Best Agent Award with a $1,000 prize arrow_outwardPythonYOLO11nMiniCPM-VNemotronllama-cpp-pythonGradioReactTypeScriptViteMUIRechartsFramer MotionComputer Vision
- Won the Best Agent Award with a $1,000 prize out of 946 apps created by hackers worldwide. Eyas is an offline CCTV intelligence agent that turns raw security footage into a structured event log using a chain of small, locally-running models with no cloud APIs, built to help convenience store owners detect shoplifting in real time instead of reviewing footage after the fact.
- Runs a four-stage pipeline entirely on CPU: YOLO11n detects and tracks people frame-by-frame; MiniCPM-V 4.6 (1.3B VLM) analyzes sub-sampled crops per tracked person and returns structured JSON observations; a heuristic event structurer converts observations into typed, zone-tagged events with timestamps using configurable evidence buffers; Nemotron 3 Nano 4B (Q4_K_M GGUF via llama-cpp-python) reasons over the structured event log to produce summaries, risk assessments, and natural-language Q&A with grammar-constrained JSON output. TinyAya handles Korean translation and VoxCPM2 generates audio briefings on CUDA-capable machines.
- Built a custom React SPA (Vite, MUI, Recharts, Framer Motion) served as static files through Gradio Blocks, replacing the default Gradio UI with a multi-camera review interface featuring resizable split panels, annotated video playback, a scatter-chart event timeline, and live pipeline progress tracking.
- Social media video filmed at Joy Convenience Store using mock camera angles. Demo footage sourced from publicly available CCTV clips, renamed and run through the full pipeline end-to-end.
Mycelium: Autonomous GitLab Knowledge Continuity Agent arrow_outward
Google Cloud Rapid Agent Hackathon
May 2026 – June 2026 GitHub arrow_outward
GitHub arrow_outward PythonFastAPIGoogle CloudVertex AIGeminiGoogle ADKGitLab MCPMongoDBMongoDB MCPMCPDockerCloud RunNext.jsReactTypeScript
- Mycelium is a fully autonomous GitLab agent that prevents engineering knowledge loss by continuously modeling ownership, expertise, and structural fragility across a codebase and acting directly inside GitLab to stabilize it.
- Infers real ownership from commits, code reviews, module interaction patterns, and temporal signals. Identifies concentrated expertise risk, orphaned subsystems, and hidden fragility from evolving codebases and forks. When issues are detected, the agent autonomously creates targeted GitLab issues, generates onboarding context packs for new contributors, and produces handoff artifacts from historical activity when engineers go inactive.
- Runs a fully autonomous Observe → Model → Analyze → Decide → Act → Reflect → Persist → Summary pipeline. Webhook-driven triggers (push, merge request, issue, and member events) activate the pipeline in real time. A CLI enables direct operation with live streaming, on-demand onboarding and offboard generation, knowledge graph inspection, and event replay. A Next.js monitoring dashboard provides full visibility into live agent traces, per-stage reasoning, knowledge graph topology, bus factor analytics, and a complete action history log.
- Built on Google Cloud Agent Builder using Gemini via Vertex AI as the reasoning model and ADK as the agent runtime on Vertex AI Agent Engine. Execution layer uses the official GitLab MCP server alongside a custom GitLab MCP server for extended continuity workflows, and MongoDB MCP for persistent graph queries. Backend is a FastAPI server deployed on Cloud Run via Docker, handling webhook ingestion, pipeline orchestration, REST APIs, and SSE streams for real-time UI updates.
Sentinel AML: Real-Time AML Intelligence Platform
2026 Confluent Data Streaming World Tour AI Day Toronto Hackathon, First Place Winner, Most Impactful AI App Award (MacBook Pro Prize)
Confluent CloudFlink SQLAzure OpenAIFlink Streaming AI AgentsKafkaPythonFlaskNext.jsData Engineering
- First place winner at the 2026 Confluent Data Streaming World Tour AI Day Toronto Hackathon, with the Most Impactful AI App Award (MacBook Pro Prize). Built Sentinel AML, a real-time anti-money laundering (AML) intelligence platform that continuously analyzes financial transaction streams to detect suspicious activity as it occurs.
- Designed for compliance teams, financial crime investigators, and risk operations units in banks and fintechs, Sentinel AML replaces batch-based compliance with a continuous, autonomous, and auditable decision pipeline.
- Detection runs in Confluent Flink SQL using tumbling-window aggregations and statistical anomaly detection to surface behaviors such as structuring, velocity spikes, and cross-border anomalies within minutes.
- Alerts are enriched via Azure OpenAI (gpt-5-mini) using Confluent's ML_PREDICT function to produce concise analyst-ready explanations inside Flink, and routed to a streaming AI agent that triages cases to escalate, review, or dismiss with recommended Suspicious Activity Report (SAR) decisions.
- The system substantially reduces detection latency and manual investigation workload while improving response speed, operational efficiency, and auditability, lowering false positives compared to batch workflows.
IMI Big Data & AI Competition 2025-2026 arrow_outward
1st Place Winner with a $15,000 prize - Hosted by Scotiabank × IMI BIGDataAIHUB
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
- 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, in the IMI Big Data & AI competition hosted by the Institute for Management & Innovation (IMI) UTM BIGDataAIHUB in partnership with Scotiabank.
- Engineered a data-intensive, interpretable AI Anti-Money Laundering (AML) system, with data analysis pipelines using 16 machine learning models across individual and small business entities, trained on customer-level features aggregated over 7 transaction types plus an all-types view in largely unlabelled financial data, capturing overall and transaction-specific anomalies.
- Designed and developed 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 that are human-readable, distinguish fraud vs. AML, and preserve auditability and factual consistency for compliance use.
- Built a full-stack Next.js web application integrating detection models, explainability, and the AML knowledge library into a unified investigator-facing workflow platform.
Clover: Drone Propulsion System Health Diagnostics
U of T ESC102 Praxis II Course Project
January 2026 – April 2026 school ESC102H1 · Praxis II arrow_outward
PythonPyTorchSupabaseFlutterNext.jsReactNumPyPandasMatplotlibJupyter NotebookGitGitHub
- Built Clover, a portable AI-powered pre-flight diagnostic system for D. Vision Aerials' FPV drones. Clover replaces manual motor spin checks with automated go/no-go assessments, improving consistency in pre-flight decisions and reducing operational risk in dense urban environments.
- The system evaluates drone health by combining acoustic motor analysis with onboard flight controller blackbox telemetry, comparing expected motor behavior against observed performance to detect and localize faults prior to flight.
- Clover runs on an edge platform (Raspberry Pi), producing real-time, time-stamped diagnostic outputs with full audit logs, and syncing results to a Supabase cloud database. Data is accessible remotely through a Flutter mobile app and a Next.js web dashboard for monitoring and review.
- For acoustic fault detection, it uses a multitask 1D CNN-ResNet architecture based on sound classification research. The deep learning model identifies fault types (motor, propeller) and infers flight direction. Audio inputs are standardized via preprocessing and domain normalization so that recordings from different drone units are mapped into the same feature distribution as the training dataset, ensuring compatibility and stable anomaly detection.
- The model was trained for 100 epochs on 324k audio samples, reaching 97.4% accuracy and F1 score on the reference dataset. In field testing, Clover achieved 81% accuracy/F1 on acoustic data and 87% on blackbox telemetry-based diagnostics.
Pong Strategic Computer AI
Bronze Medalist in the U of T EngSci Pong AI Tournament 2025
November 2025 – December 2025 school ESC180H1 · Introduction to Computer Programming arrow_outward emoji_events 2025 U of T EngSci Pong AI Tournament, Bronze Medal arrow_outward
PythonPygameVisual Studio Code
- Bronze Medalist in the U of T EngSci Pong AI Tournament 2025. Developed an advanced computer AI for Pong that achieved a 50-1 win record against a chaser opponent (a simple player that follows the ball's Y-position).
- Implemented physics-based bounce simulation with accurate wall and paddle collision detection, and a two-bounce prediction algorithm that anticipates the ball's trajectory through multiple rebounds.
- Engineered offensive strategies that aim for paddle edges to generate unpredictable bounce angles, exploiting paddle angle mechanics to maximize scoring opportunities.
- Designed a detection sequence system to dynamically identify paddle positioning, and simulated opponent paddle movement to predict future game states for strategic decision-making.
StackDAG arrow_outward May 2025 – Sep 2025
Next.jsReactJSXTSXTypeScriptFirebaseSupabase Cloud DatabasePostgreSQLSQLOpenAIGitHubVisual Studio Code
- Designed and developed StackDAG, a full-stack AI-integrated web application built with Next.js (React), Firebase authentication, and a Supabase cloud PostgreSQL database with Supabase Edge Functions for REST request processing and API security.
- Enables users to view, create, share, fork, and upvote Directed Acyclic Graphs (DAGs) representing technology stacks.
- Integrated the OpenAI API to provide layer-by-layer setup and integration guidance for each DAG, with all DAGs and AI-generated instructions stored in Supabase for fast retrieval.
DOTA Aerial Object Detection using YOLOv11
August 2025 GitHub arrow_outward
GitHub arrow_outward PythonYOLOPyTorchNumPyPandasMatplotlibOpenCVGoogle ColabGitGitHub
- Developed an end-to-end deep learning aerial object detection pipeline using YOLOv11 Nano for the DOTA v1.5 dataset, focusing on preprocessing and data transformation for oriented bounding boxes.
- Implemented image tiling (640×640 with padding for YOLO format) to preserve resolution and detail, translated and clamped OBB annotations to tile boundaries, converted polygons to YOLO-oriented format with normalized coordinates, and mapped class names to indices.
- Trained and evaluated the YOLOv11 model on the processed dataset, producing detection visualizations, loss curves, and confusion matrices. Developed and documented my complete workflow and experimentation process in a Google Colab-hosted Jupyter Notebook.
HAM10000 Dataset Skin Lesion Classification using MobileNetV2
August 2025 GitHub arrow_outward
GitHub arrow_outward PythonTensorFlowNumPyPandasMatplotlibGoogle ColabGitGitHub
- Developed an end-to-end deep learning pipeline for multi-class skin lesion classification using the HAM10000 dataset, using transfer learning with MobileNetV2 as the backbone.
- Addressed severe class imbalance through targeted data augmentation and stratified train/validation/test splits.
- Evaluated model performance with accuracy, loss curves, and confusion matrix analysis to identify common misclassifications. Developed and documented the end-to-end workflow, challenges, and results in a Google Colab-hosted Jupyter Notebook.
Wellington Zone 1 Power Consumption Predictions
July 2025 GitHub arrow_outward
GitHub arrow_outward PythonPandasNumPyscikit-learnMatplotlibAnacondaJupyter NotebookGitHub
- Developed an end-to-end machine learning pipeline for Wellington Zone 1 Power Consumption Predictions using a Kaggle dataset of environmental and time series factors.
- Performed feature engineering, model evaluation, and selected Ridge Regression, achieving an R² score of 0.9963 on the test set with 382 ms prediction time. Developed and documented my workflow and experimentation step-by-step in a Jupyter Notebook.
Tourista Hack the North 2023
September 2023 GitHub arrow_outward
GitHub arrow_outward SvelteSvelteKitTypeScriptFirebaseCohereGoogle MapsGitGitHubVisual Studio Code
- Tourista is an AI-driven travel guide that offers personalized travel recommendations.
- Integrated Cohere's AI API for intelligent location suggestions, Google Maps APIs for dynamic route mapping, and implemented Firebase authentication with clear, user-friendly messaging.
FlexChat July 2023 – August 2023 GitHub arrow_outward
GitHub arrow_outward AngularTypeScriptHugging FaceHugging Face TransformersGitHubVisual Studio Code
- FlexChat is a platform for testing and comparing Hugging Face AI chatbot models.
- Designed and developed the full-stack Angular application integrating Hugging Face APIs for model experimentation, with a user-friendly chat interface for creating, editing, and managing multiple chats to compare outputs from different models.