I am a student with a deep passion for Machine Learning, Data Science, and low-level programming. I specialize in building complex systems from absolute scratch to truly master the mechanics. As an autistic developer, I bring a unique, highly-focused perspective to problem-solving : while I thrive in deep-work environments, I view this neurodivergence as a powerful asset for technical innovation and am eager to contribute my dedication to your projects.

I suggest you have a look at especially one of these :

• TurboQuand by google deepmind in tensorflow
• LLM From Scratch
• LAFF - Linear Algebra Foundations to Frontiers

• LLM From Scratch: A large language model built entirely in C++ from the ground up, featuring custom self-attention heads and transformer architectures.
• GBDT for Price Prediction: A Gradient-Boosted Decision Tree model created from scratch in C++ to predict housing prices.
• Agentic RAG: Currently developing a private, advanced Agentic Retrieval-Augmented Generation system for SopraSteria using Python.
• Network Anomaly Detection: Designing an ML model from scratch to identify suspicious behaviors in network traffic, my end of school year project.

• Infinite-Runner: A school project: a 2D game built with Python and Pygame where players dodge obstacles, collect coins, and manage lives, it was built under strict 6 hour constraints, beside studies and discovering of pygame library.

• LAFF - Linear Algebra Foundations to Frontiers: Implementing algorithmic optimizations and linear algebra concepts from the University of Texas - Austin, entirely in C++ from scratch. (Verified Certificate)
• TP1-Genericite & Paradigme-Programmation: Various Java projects exploring object-oriented programming, class inheritance, and polymorphism.

Languages I excel at:

• C++ / C: Used heavily for from-scratch ML algorithms, and deep performance optimizations.
• Python: For rapid prototyping, Neural Networks, Pygame, and data engineering and its richness of libraries.
• Java: For strong algorithmic foundations and object-oriented paradigms.
• Web: HTML, CSS, JavaScript.

Mathematical focus:

• Linear Algebra (Matrix optimizations)
• Probability & Statistics (MIT curriculum)
• Core Machine Learning Algorithms (GBDT, Transformers, Neural Networks..)

I am curious, continuously learning. Whether I'm debugging, tuning hyper-parameters for a new ML model, or just learning,

My dream would be to work at Google as a Machine Learning Engineer, their project are always innovative and i would love to be a part of it. Thanks for reading my profile, feel free to contribute to my projects or ask me any question you may have.