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 preference for building working prototypes rather than just reading about them.
A mix of:
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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
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Programming languages & systems
- Some Lisp Machine emulation
- Small tools, templates and experiments in various languages
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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.