Pierre Paleo
Data Analysis Engineer
Grenoble, Auvergne-Rhône-Alpes, France
Summary
👤
Senior🎓
Top SchoolPierre Paleo is a Data Analysis Engineer with 13 years of experience applying signal processing and high-performance computing to scientific imaging and real-time video analysis. Based in Grenoble, he designs and implements GPU-accelerated image reconstruction and optimization algorithms for large-scale tomography and dynamic scattering pipelines at ESRF, combining applied mathematics with pragmatic software engineering. His background includes PhD-level research in advanced reconstruction methods and practical R&D building scalable, near-real-time computer vision systems using Python, CUDA/OpenCL, and Go. He also contributes to open-source testing and performance improvements for widely used projects like pycuda, reflecting a strong focus on correctness and efficiency. Known for tackling bottlenecks across distributed and streaming data architectures, he blends academic rigor with production-grade robustness.
12 years of coding experience
1 year of employment as a software developer
Mathématiques, Physique, Mathématiques, Physique at Lycée Victor Hugo, BesançonBaccalauréat scientifique, Mention Très Bien, Baccalauréat scientifique, Mention Très Bien at Lycée Armand PeugeotEngineer's degree, Signal processing, Informatics, Telecommunication, Honors, Engineer's degree, Signal processing, Informatics, Telecommunication, Honors at Grenoble Institute of Technology (Grenoble INP - Phelma)
French, English, German
Github Skills (11)
gpgpu10
python10
pycuda10
accelerated-computing10
testing10
numpy9
gpu9
arrayobject8
scientific-computing7
pytest7
cuda6
Programming languages (4)
Github contributions (5)
inducer/pycuda
Sep 2022 - Sep 2022
CUDA integration for Python, plus shiny features
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:2 reviews, 15 commits, 1 PR in 7 days
Contributions summary:Pierre focused on improving the test suite for the pycuda library. They added new tests for reduction and elementwise cases, and also simplified existing tests. Furthermore, the user addressed performance bottlenecks by optimizing a test related to big array elementwise operations. These changes highlight a focus on ensuring the library's correctness and performance.
cudaarraypythongpuscientific-computing
pierrepaleo/PDWT
Feb 2016 - Oct 2020
A GPU implementation of the Wavelet Transform
Contributions:1 release, 59 commits, 1 PR in 4 years 8 months
cudawaveletwavelet-transformtransformgpu
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