Computer Science student with a strong interest in mathematics, algorithms and
systems, and a solid background in data science.
I enjoy understanding how things work under the hood and building tools that reflect
that understanding.
I’m currently studying Computer Science, driven mainly by curiosity and a genuine interest in the fundamentals behind software, algortithms, data and complex systems.
What motivates me the most is not a specific domain, but the process of reasoning:
modeling problems, understanding constraints, designing algorithms and translating
ideas into working systems.
So far, the area where I’ve gone deeper in practice is Data Science, but my interests are much broader and naturally extend to systems programming, graphics, computer vision and machine learning — always from a technical and mathematical perspective.
At the moment, most of my hands-on experience revolves around Data Science, including:
- Working with data
- Exploratory analysis and statistical reasoning
- Building and evaluating models
- Implementing algorithms from scratch
- Designing reproducible analysis pipelines
This has been my main practical entry point into applying mathematics and programming to real-world problems.
Beyond what I’ve worked on the most so far, I’m deeply interested in:
- Algorithms and data structures
- Mathematical modeling
- Systems and tool development
- 3D graphics and game engines (mainly for the mathematics behind them)
- Computer vision
- Machine learning, as a tool — not a goal in itself
I’m particularly drawn to areas where understanding the theory and the implementation details really matters.
One of the domains that has consistently caught my attention is algorithmic trading, approached from an engineering and scientific standpoint.
Not discretionary trading, but:
- markets as complex systems
- data as raw material
- models as hypotheses
- decisions driven by analysis, statistics and reproducibility
It’s a domain where mathematics, algorithms and systems naturally intersect.
A Python-based project where I implement the K-means algorithm from scratch using only mathematics and NumPy, including 3D visualization of clustering results applied to gold price data.
Built purely as a learning exercise, focusing on understanding both the algorithm and its behavior.
A Python-based project implementing both a single Perceptron (artificial neuron) and a Multi-Layer Perceptron (MLP) entirely from scratch, using only mathematical formulations and NumPy.
The goal is to understand neural networks from first principles, without relying on ML frameworks.
A project focused on detecting support and resistance zones by combining a rolling Market Profile approach with a Kernel Density Estimator (KDE) implemented from scratch using NumPy.
Designed with a strong emphasis on statistical reasoning and interpretability.
A data-driven tool designed to help farmers and gardeners identify optimal locations in Spain to cultivate a specific plant.
Based on meteorological station data, the application provides a visual representation of expected growth performance across different regions.
A Python-based web application designed to streamline the analysis of electricity consumption reports for companies, providing insights in a faster, cleaner and more visual way.
To be continued...
C++: currently learning, motivated by low-level control, performance and 3D-related mathematics.
- I care about fundamentals and trade-offs
- I prefer understanding models rather than treating them as black boxes
- Most of my experience comes from building things from scratch to truly learn how they work
- I’m more interested in robust reasoning than quick results
- I value clarity, structure and reproducibility
I’m open to working across different industries and domains, especially those where software is a tool rather than the end goal.
I’m particularly interested in environments where programming is used to model, simulate, analyze or control complex systems, such as robotics, medical technologies, defense, or other highly technical fields.
What motivates me is not the domain itself, but the challenge of understanding it deeply and building robust, well-reasoned tools on top of solid mathematical and algorithmic foundations.