Computer Science student at Assumption University of Thailand
AI, machine learning, computer vision, recommender systems, and practical software engineering
I am a B.Sc. Computer Science student building applied AI and software projects with an emphasis on evidence, privacy, testing, and practical usability. My current work spans evidence-first AI research systems, recommender systems, local document intelligence, browser applications, and AI developer tooling.
I learn primarily by building complete systems: designing the architecture, implementing the product, testing failure cases, evaluating model or recommendation quality, and documenting the trade-offs.
| Project | What it demonstrates |
|---|---|
| UniProof | Evidence-first AI university research, comparison, and application guidance with Next.js 16, React 19, TypeScript, Supabase/PostgreSQL RLS, structured multi-provider AI, Vitest, and Playwright |
| Groovehaus Frontend + Backend | Full-stack vinyl recommender system with React/Vite, Next.js, MongoDB/Mongoose, explainable recommendations, personalization controls, and offline recommender evaluation |
| OCR Model | Local document intelligence using PaddleOCR, LayoutXLM, PyTorch, OpenCV, schema-validated extraction, Docker, and an optional privacy-gated Codex/MCP review workflow |
| Codex Router | Local model-routing and Codex integration work around Responses API compatibility, provider adapters, credential isolation, Windows reliability, and model-catalog integration |
| Codex ChatGPT Control Plus | TypeScript and Python SDK/tooling for visible, user-directed ChatGPT workflows through Codex, browser control, plugins, contracts, and release validation |
| Rubik's Cube | Standalone Three.js/WebGL Rubik's Cube with manual interaction, solver-assisted recovery, Vite, Vitest, and workflow verification |
| Agent Skills | Reusable AI-agent skills, validation workflows, and cross-client developer tooling |
- Evidence-grounded AI systems with deterministic validation and explicit uncertainty.
- Machine learning, computer vision, OCR, and document information extraction.
- Recommender systems, offline evaluation, ranking, personalization, and decision support.
- Full-stack TypeScript/JavaScript applications with strong testing and security boundaries.
- AI developer tooling using Codex, ChatGPT, MCP, model routing, and agent workflows.
AI and agent tooling: OpenAI Codex, ChatGPT, Model Context Protocol (MCP), agent skills, model routing, browser automation, GitHub Actions
CSX3009- Algorithm DesignCSX4207- Decision Support and Recommendation SystemCSX3004- Programming LanguagesCSX4213- Computer VisionCSX4201- Artificial Intelligence ConceptsCSX4203- Machine LearningBBA1004- Essential Marketing for Entrepreneurs
Selected completed coursework
CSX2009- Cloud ComputingCSX3005- Computer NetworksCSX3006- Database SystemsCSX4107- Web Application DevelopmentITX2007- Data ScienceCSX2008- Mathematics Foundation for Computer ScienceCSX3001- Fundamentals of Computer ProgrammingCSX3002- Object-Oriented Concepts and ProgrammingCSX3003- Data Structures and AlgorithmsITX2004- UI/UX Design and PrototypingITX2005- Design ThinkingITX3007- Software EngineeringITX3003 / CSX4401- Business Systems
- GitHub: PracticalSwan
- LinkedIn: sithuws17
- Email: sithuwinsan2007@gmail.com


