Welcome ----

I work at the intersection of software engineering, data / machine learning, and quantitative problem‑solving. Many of my projects come from curiosity about how things work (markets, systems, languages) and a desire to building systems rather than reading about them.

A mix of:

• Machine learning & statistics

  • Time series forecasting with transformers and deep learning
  • Classical ML / data science projects (e.g. scoring, recommender systems)
  • Experiments with modern tooling in Python, Julia and R

• Programming languages & systems

  • Some Lisp Machine emulation
  • Small tools, templates and experiments in various languages

• Learning, notes & talks

  • Course work and notes
  • Slides and code from meetups and presentations

Some repositories are exploratory or educational, others are closer to production‑style prototypes.

• Languages: Python, Julia, R, Rust, C/C++, Java, TypeScript
• ML / data: PyTorch, scikit‑learn, statistical modeling, time series, optimisation
• Tools: Git, Linux, Jupyter, Docker, SQL

Always combining solid mathematical reasoning with practical engineering, whether that’s for data‑driven projects or systems programming.

• Start from a clear question or use case
• Keep the first version simple and measurable
• Iterate and refactor toward clarity and robustness
• Document enough for future‑me (and others) to understand what’s going on

• Blog / notes: https://emmanuel-r8.github.io
• Another organisation I use for projects: https://github.com/Alba-Intelligence
• LinkedIn: https://www.linkedin.com/in/emmanuelrialland/

If something here sparks your interest, feel free to open an issue, a pull request, or just reach out.