Applied Mathematics student building real AI — from theory to deployed models.

Mathematics First
My foundation is mathematical. At UMP's Faculty of Applied Mathematics for Machine Intelligence, I study optimization theory, probability, stochastic analysis, fractal geometry, mathematical finance, topology, cryptography, and quantum computing — the rigorous underpinnings that make AI models not just work, but work reliably and explainably.

From Theory to Production
During a 6-month internship at Ainubes GmbH (Aug 2025 – Feb 2026), I developed and deployed an AI model that detects anomalies in residential energy consumption time series across a large German housing dataset. I also worked on Archivfy, an intelligent document platform with OCR-based classification and semantic search.

Research & Engineering Mindset
I come to AI from a software engineering background — Java, Spring Boot, MERN — which gives me an unusual ability to move between writing a research-grade statistical model and shipping a production-ready API. I write about AI on Medium, open-source my experiments on GitHub, and continuously work on deepening my ML and mathematical toolkit.

What I'm Building Toward
My goal is a PFE internship (end-of-study project, starting February 2027) at a leading Moroccan or international company working on serious data problems — whether in energy, banking, telecom, or research. After that, I want to contribute to AI teams building systems that have measurable, real-world impact.