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shawn-cse/readme.md

Shawn

Software Engineer · AI Engineer

Building practical software across full-stack engineering, machine learning, security, APIs, automation, and developer tooling.

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About

I’m a Computer Science & Engineering graduate focused on building software that is useful, understandable, and dependable.

My projects span responsive web applications, backend APIs, local and server-side data workflows, machine-learning pipelines, security-focused tools, automation, and practical deployment patterns. I care about the complete engineering lifecycle: understanding constraints, designing a sensible architecture, implementing cleanly, validating edge cases, documenting decisions, and improving through iteration.

const shawn = {
  roles: ["Software Engineer", "AI Engineer"],
  builds: ["full-stack systems", "ML pipelines", "secure tools", "automation"],
  values: ["clarity", "reliability", "useful UX", "continuous improvement"],
  principle: "Prefer purposeful engineering over unnecessary complexity."
};

Engineering Focus

Area What I work on
Software Engineering Responsive applications, backend services, REST APIs, data persistence, validation, debugging, maintainable architecture
AI & Machine Learning Data preparation, feature engineering, classification, ensemble models, evaluation, computer vision, experimentation
Security & Privacy Browser-side cryptography, secure session patterns, input validation, rate limiting, privacy-conscious analytics
LLMs & Automation Prompt design, LLM/API integrations, agents, tool-based workflows, process automation
Delivery Git, GitHub Actions, Docker, Linux, Bash, testing, documentation, deployment-oriented development

Featured Projects

Full-stack URL shortener with user accounts, custom aliases, expiry controls, link management, redirects, and click analytics.

Engineering highlights

  • Node.js backend and SQLite persistence
  • CSRF protection, rate limits, prepared SQL, secure cookies
  • Privacy-conscious analytics without raw IP or raw user-agent storage
  • Docker support, tests, documentation, GitHub Pages frontend preview

JavaScript Node.js SQLite Security Docker

Privacy-first password security toolkit built with modern TypeScript.

Engineering highlights

  • Web Crypto based password and passphrase generation
  • Local strength analysis and entropy estimation
  • Optional k-anonymous breach lookup
  • Fastify API, tests, CI, Docker, PWA shell, accessibility support

TypeScript Fastify Web Crypto PWA CI

End-to-end machine-learning pipeline for loan approval classification and applicant ranking.

Engineering highlights

  • Preprocessing, feature engineering, scaling, stratified split
  • Naive Bayes uncertainty flags for human review
  • A* inspired applicant ranking
  • Random Forest and XGBoost evaluation with F1, ROC-AUC and cross-validation

Python scikit-learn XGBoost Pandas Jupyter

Responsive document-tool frontend designed for clear static hosting and practical browser workflows.

Engineering highlights

  • Searchable tools catalogue
  • Browser-ready vs server-dependent workflow clarity
  • Dark mode and responsive mobile/desktop layouts
  • GitHub Pages friendly architecture with no build step

JavaScript HTML CSS GitHub Pages

Inventory management dashboard for product tracking, low-stock visibility, valuation, filtering, and CSV export.

JavaScript LocalStorage CSV Responsive UI

The rest of my GitHub includes algorithms, coursework, application experiments, security utilities, learning repositories, and additional web projects.

I use these repositories to practise implementation quality, project structure, documentation, and problem solving across different areas of computer science.

Technical Toolkit

Languages

Python JavaScript TypeScript C C++ Java HTML5 CSS3

AI / ML / Data

PyTorch TensorFlow scikit-learn Pandas NumPy OpenCV Hugging Face

Backend / Engineering

Node.js Express Fastify SQLite Git GitHub Actions Docker Linux

GitHub Snapshot

Shawn's GitHub statistics Most used languages across Shawn's public repositories

How I Like to Work

  • Start from the problem and constraints, not the framework.
  • Keep interfaces and APIs predictable.
  • Treat validation, error states, security, and accessibility as engineering requirements.
  • Use AI/ML where it contributes meaningful product value.
  • Document enough that the next engineer can understand the system quickly.
  • Prefer small, testable iterations over large opaque changes.

Contact

I’m open to software engineering opportunities, AI/ML projects, technical collaboration, and open-source work.


Build useful systems · Solve meaningful problems · Keep improving

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