Alessia Tricomi is a Remote Sensing Data Scientist with 12 years of experience applying machine learning and deep learning to Earth Observation data, currently developing crop classification and vegetation-monitoring solutions at e-GEOS. She combines a rigorous mathematical background (M.Sc. with honors) and a Second Level Master in Space Science to bridge theory and operational EO analytics, including hyperspectral data exploitation. Proficient in Python, PostgreSQL, Keras/TensorFlow, PyTorch, Scikit-learn and GDAL, she designs prototypes through to beta testing and operational pipelines for big geospatial datasets. As Emergency Manager for the Copernicus Rapid Mapping Service she also brings proven experience in high-pressure, mission-critical workflows. Known for a strong research mindset and customer-focused problem solving, she frequently translates state-of-the-art methods into practical pilots. Based in Florence, she pairs academic distinction with hands-on EO product delivery across multi-temporal and multi-sensor projects.
12 years of coding experience
Sponsored by ASI, SIMCA, GNFM (Gruppo Nazionale per la Fisica Matematica), SpaceDys, Sponsored by ASI, SIMCA, GNFM (Gruppo Nazionale per la Fisica Matematica), SpaceDys at Satellite Dynamics and Space Missions Summer School
Bachelor Degree, Mathematics, Bachelor Degree, Mathematics at Università degli Studi di Firenze
Summer school on Optimization, Big Data and Applications
110/110 cum laude, 110/110 cum laude at Liceo Classico Michelangiolo, Firenze
Second Level Master, Space Science and Technology, 110/110 cum laude, Second Level Master, Space Science and Technology, 110/110 cum laude at Università di Roma Tor Vergata
Contributions:4 commits, 2 PRs, 18 comments in 3 years 2 months
cmscernweb-appc-plus-plusbackbonejs
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