Devis Peressutti
MLE at Planet
Ljubljana, Slovenia
Summary
🤩
Rockstar🎓
Top SchoolDevis Peressutti is a machine learning engineer with a decade of experience applying statistical and deep learning methods to remote sensing and medical imaging problems. Currently at Planet, he focuses on making large-scale EO AI operational, having previously led ML work at Sinergise and prototyped radiotherapy contouring systems at Mirada Medical. His contributions to prominent open-source projects like eo-learn and sentinelhub-py highlight practical expertise in raster data processing, coregistration and production-ready backend tooling. Trained as a Medical Imaging PhD from King’s College London, he blends rigorous research on organ and respiratory motion with hands-on deployment of ML pipelines. Colleagues value his rare combination of domain knowledge in organ motion estimation and pragmatic engineering for scalable geospatial ML. He is based in Ljubljana and consistently bridges academic insight with industrial-scale EO solutions.
10 years of coding experience
11 years of employment as a software developer
M/Phd, Medical Imaging and Biomedical Engineering, M/Phd, Medical Imaging and Biomedical Engineering at King's College London, U. of LondonAlta Scuola PolitecnicaMaster's degree, Biomedical/Medical Engineering, Master's degree, Biomedical/Medical Engineering at Politecnico di Torino
Italian, English
Github Skills (11)
machine-learning10
python10
image-processing10
spatial-data10
testing9
geometric-algorithms9
gis9
geographical-information-system9
computer-vision8
lightgbm8
aws6
Programming languages (3)
Github contributions (5)
sentinel-hub/eo-learn
Jun 2018 - Dec 2022
Earth observation processing framework for machine learning in Python
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:16 reviews, 116 commits, 41 PRs in 4 years 6 months
Contributions summary:Devis implemented support for generating mappings from multi-class PNG Geopedia requests within the eo-learn library, demonstrating a focus on handling and processing raster data. They also refactored the snow masking tasks. Moreover, the user was involved in the coregistration sub-package refactoring efforts and created a new ECC coregistration task. The user has added several tests and made improvements in the codebase.
satellite-imagerypythonobservationearth-observationearth
sentinel-hub/sentinelhub-py
Feb 2019 - Jul 2019
Download and process satellite imagery in Python using Sentinel Hub services.
Role in this project:Back-end Developer
Back-end DeveloperContributions:18 commits, 2 PRs, 10 pushes in 5 months
Contributions summary:Devis primarily contributed to the development of backend functionality within the Sentinel Hub Python library. Their work involved modifying configuration files, adapting geometry methods, and implementing new features related to UTM grid splitting. They also addressed testing issues and incorporated data files into the package, demonstrating a focus on core library functions and spatial data processing.
python-librarysatellite-imageryrasteriopythonsar
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