Open source
My strongest work sits in private company repositories. What is public is a mix of backend groundwork, data analysis, and the physics coursework where the numerical methods came from. I would rather show you that honestly than pad a grid.
Python
A FastAPI starter with the authentication layer already solved: OAuth2 with signed JWTs, async PostgreSQL through SQLModel, and login by username, email or phone against one identity. The closest public equivalent to the auth work described in the case studies.
A FastAPI service for common celestial-mechanics calculations. Physics and backend in the same repository, which is roughly how the two halves of my work meet.
Python
The daemons running on my own Linux server. Small, unglamorous, and the reason I am comfortable claiming systemd and Debian administration on a CV.
Python
A multipurpose Telegram bot, including a dynamic-DNS updater for a home server on a changing IP.
Python
Rental and sale prices for apartments in Bogotá: extraction, cleaning, visualisation and predictive modelling. A rework of an existing open-source dataset project rather than a fork left untouched.
Jupyter
Financial analysis for a dental clinic, reading from MySQL and analysed in Pandas. Real books rather than a sample dataset.
Jupyter
A Monte Carlo study of a Monopoly board: which squares actually pay, and how long it takes to find out.
Jupyter / C++
Fourier analysis, ordinary differential equations and PDEs, in C++ and Python. University coursework, kept public because it is where the numerical methods behind the Kalman filtering work were actually learned.
Interpolation, numerical integration, Monte Carlo, least squares and hidden Markov models. Coursework, paired with another student.
Jupyter
Physics coursework and lab analysis from the degree at Universidad de los Andes: modern physics, waves and fluids, mechanics, astrophysics.