ADRIAN LAYNEZ ORTIZ
Mathematics & Computer Science.
Mechanistic Interpretability · High-Performance Engineering.
Research Sections
Interactive Visualizations
Languages
Curiosity
Deep Learning Engine — CUDA / C++
Custom kernels for matrix operations and backpropagation
Bridging Abstract Mathematics
& Machine Intelligence
I am pursuing a double degree in Mathematics and Computer Science at the Universidad Complutense de Madrid. My research focuses on understanding neural networks at their deepest level — from gradient dynamics to kernel-level optimization.
I specialize in Mechanistic Interpretability — the science of reverse-engineering how neural networks represent and process information internally. Rather than treating models as black boxes, I decompose their circuits to understand why they work.
My mission: make AI systems transparent through rigorous mathematical analysis and low-level engineering.
Technical Proficiencies
Engineering from
First Principles
Every project begins with a question. From reimplementing seminal papers to writing bare-metal GPU kernels, each one is an exercise in deep understanding.
Let's Build Something
Together
Whether it's a research collaboration, an internship opportunity, or just a conversation about the mathematics of intelligence — I'd love to hear from you.