Gonzalo García is an applied machine learning engineer and data scientist with over a decade of experience applying deep learning to Earth observation and environmental monitoring. He has led R&D efforts across academia, industry and international organizations—building satellite-based solutions for methane detection, flood and cloud segmentation, super-resolution, crop yield forecasting and water-quality assessment—and currently automates methane-emission detection for UNEP’s IMEO and the Methane Alert and Response System. Equally comfortable in research and production, he has contributed open-source tools (e.g., ml4floods), led ESA-funded projects, and delivered onboard and backend systems for operational monitoring. Trained to doctorate level in machine learning and remote sensing, he blends rigorous evaluation and domain adaptation expertise with full-stack engineering experience in meteorology and energy forecasting. Less obvious: his work spans not only model research but also deployable pipelines and satellite onboard processing, enabling real-time, accountable environmental alerts at scale.
11 years of coding experience
6 years of employment as a software developer
Master in Statistical-Computational Data Processing, Statistics, 9.01 (A), Master in Statistical-Computational Data Processing, Statistics, 9.01 (A) at Universidad Complutense de Madrid
Doctor's Degree, Machine Learning and Remote Sensing, Sobresaliente Cum Laude, Doctor's Degree, Machine Learning and Remote Sensing, Sobresaliente Cum Laude at University of Valencia
Exchange Student, Computer Science and Mathematics, Exchange Student, Computer Science and Mathematics at Technical University of Denmark
double major in Computer Science and Mathematics, Machine Learning, 7.56 (B), double major in Computer Science and Mathematics, Machine Learning, 7.56 (B) at universidad autonoma de Madrid
Multitemporal Cloud Masking in the Google Earth Engine
Contributions:57 commits, 23 pushes, 1 branch in 3 years 11 months
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