Javier Saez Gallego

The project answers the following simple question: Can a squirrel cross from north to south Spain without touching the ground?

The answer is: obviously not. But, another question arises, and this one is not so easy to answer: If a squirrel had to go from the north of Spain to the south, touching the ground as little as possible: Which way would it follow?

The answer is not trivial, and it is answered here!

The project consisted of the following steps:
1. Download data from the internet. The data consists of GIS information to determine whether a piece of land is a forest, a river, a man-made construction, and so on.
2. Create a path matrix. The squirrel takes one step at a time. In terms of a raster map, this means we can reach each pixel only from adjacent pixels. Think of it as a huge spare matrix.
3. Optimize the path. Calculate the shortest path from the top of Spain to the bottom. The cost of each segment is the pixel value so forest costs nothing to move across and anything related to water is impossible to cross.
4. Visualize the solution as an interactive map.

A Python package for bundling notifications in event streams. The goal is to minimize the number of notifications sent to users and to not send more than four notifications per day.

A full-stack application developed from scratch to populate any app or design with beautiful customized avatars generated with AI. Thousands of avatars are available for many pre-existing styles, and the possibility to generate your own. Includes a Python-based API deployed to a Kubernetes cluster.

I created a full-stack application that retrieves historical weather data from Open-Meteo and generates straightforward graphs illustrating climate changes worldwide. It leverages Google location services to suggest places for users. This project was built using Nuxt, Tailwind, and FastAPI and deployed on a Kubernetes cluster. "ShowYourLocalStripes" aims to demonstrate climate warming in a user-friendly way, drawing inspiration from Ed Hawkins' original #showyourstripes movement, with a focus on local warming trends.

I designed a deep-learning model and implemented a back-end system for avian species classification using audio data. The model exhibits a distinctive capability to discriminate between bird songs and calls within the same species. The back-end infrastructure has been successfully deployed on Amazon Web Services (AWS) and processes over 500 requests per hour during peak times.