In 2023, I was part of the "Rocket Hacks" team, which was honored as a Global Finalist in the NASA Space Apps Challenge, the largest hackathon in NASA's history. This global challenge drew 57,999 participants from 152 countries, our project distinguished itself among the top 40.Our initiative was dedicated to developing a Convolutional Neural Network + Long Short Term Memory (LSTM) model that achieved an 82.35% accuracy in predicting the trajectory of wildfires. Our goal was to provide critical notifications to underserved regions with limited access to the internet and energy resources. We harnessed key technologies such as React.js and Next.js to craft an interactive and responsive web app, TypeScript to ensure the system's reliability, Mapbox for real-time 3D visualization of fire spread, Tailwind CSS for efficient user interface design, and Vercel as a dependable platform for deployment and hosting.To fuel our predictive model, we leveraged valuable data from various sources, including the Moderate Resolution Imaging Spectroradiometer (MODIS), the Meteomatics API for comprehensive meteorological data, and the Fire Information for Resource Management System (FIRMS). It's worth noting that this information was specifically collected from the Nogales, Sonora area, where we conducted a 64 x 64-kilometer sampling, enabling our model to make precise predictions about fire trajectories within that region.To effectively alert rural communities, we developed a functional prototype known as the Emergency Response Handheld Device Prototype (ERHD), which operates using solar panels, in which we implemented with LoRaWAN technology, enabling us to transmit alerts even in areas with limited access to conventional communication infrastructure.

Member of the high school robotics team, in which we used tools such as programming, mechanics and electronics to be able to make a robot for a competition held in Monterrey, Nuevo Leon. In this competition we managed to reach the semifinals.