Paul Estano
Deep Learning Research Engineer at IDEMIA
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolPaul Estano is a Deep Learning Research Engineer based in Paris with eight years of experience bridging academic rigor and industrial impact across semiconductors, GPU software, and document authentication. He holds two engineering master’s degrees, including one from a top-11 global program, and has driven research on low-precision optimization for deep learning at Inria while deploying applied ML solutions at IDEMIA. Paul combines strong expertise in computer vision, optimization, and deep learning with practical systems experience from roles at Arm, GE Healthcare, and SAP. He is also an active open-source contributor, having improved SciPy’s BFGS optimizer to enhance performance and stability for positive-definite problems—an indication of his attention to numerical robustness. Known for teaching and mentoring graduate students, he blends clear pedagogy with hands-on engineering to move algorithms from theory to production.
8 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Electrical and Computer Engineering, Master of Science - MS, Electrical and Computer Engineering at Georgia Institute of TechnologyMaster of Engineering - MEng, Computer Engineering, Master of Engineering - MEng, Computer Engineering at Ecole nationale supérieure de l'Electronique et de ses ApplicationsBaccalauréat, Sciences, Baccalauréat, Sciences at Lycée Montgrand
English, French
Github Skills (11)
algorithm10
algorithms10
scipy10
python10
optimizers10
optimisation10
scientific-computing10
numpy10
optimization10
linear-algebra9
unit-testing9
Programming languages (4)
Github contributions (5)
scipy/scipy
Feb 2022 - Sep 2023
SciPy library main repository
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 reviews, 1 PR, 22 comments in 1 year 7 months
Contributions summary:Paul primarily contributed to the optimization of the BFGS optimization algorithm within the SciPy library. Their work involved adding a parameter for initial inverse Hessian estimation and addressing comments, resolving issues, and improving the code quality. They also modified the testing suite to include new tests for the implemented features, particularly focusing on the handling of positive-definite matrices. These changes involved modifications to core optimization routines, impacting the performance and stability of the BFGS method.
scipypythonscientific-computing
paulesta55/estanocandassamy
Sep 2018 - Jul 2019
final year ENSEA/IS project
Contributions:218 pushes, 5 branches, 83 tags in 9 months
final-year
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