I'm Caden, a Mathematics student at the University of Waterloo working in the fields of Machine Learning, AI Engineering, and the software that holds them together. Right now I'm helping deploy AI systems in production at ProCogia and helping professor Jimmy Lin with retrieval (RAG) research. You can find my projects in this repo. My favorite ones are under the Featured Projects section.
What I'm currently working on (click to expand)
- AI Intern @ ProCogia - built a LangGraph-orchestrated calling-agent POC (~500 ms faster, a 3x gain over Amazon Connect) plus a RAG pipeline on AWS S3.
- Jimmy Lin's Research Group @ Waterloo - information-retrieval research with the Anserini / Pyserini toolkits.
- ML Engineer @ Wat Street (Waterloo's Quantitative Finance team) - wiring a Graph Attention Network into an LSTM in PyTorch to forecast volatility for 500 S&P stocks.
- Software Developer @ WARG (past) - shipped client-side mission-export tooling for the IMACS-3.0 ground control station (React + QGC
.waypoints).
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End-to-end flight-price prediction across 200+ routes and 55+ airports. A SQLite pipeline ingests ~4k offers/day (400K+ rows) to train an XGBoost model targeting sub-10% error. |
A fine-tuned ResNet-18 that spots ragdoll cats (0.81 F1), exported to ONNX and running entirely in the browser.
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My personal portfolio — a responsive Next.js / React / TypeScript site styled with Tailwind CSS and deployed on Vercel.
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A full-stack chess AI: a PyTorch evaluator (768 to 256 to 128 to 1) served via a Flask API and React/TypeScript frontend, Dockerized and deployed on Vercel with sub-second moves.
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A Python package for reranking search results with LLMs (pointwise, pairwise, and listwise rerankers) — one of Professor Jimmy Lin's group repositories, which I work with as part of his research group. |
A deep-learning study comparing models for classifying chest X-rays as normal vs. abnormal. |
Languages
Machine Learning & Data
Frameworks & Backend
Tools & Platforms