Nicolas Guigui
CTO & Co-founder at CNRS - Centre national de la recherche scientifique
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolNicolas Guigui is a CTO and co-founder with 8 years of experience building machine learning and real-time 3D imaging systems, currently leading OWLO to commercialize non-invasive microscopy for healthcare. His background spans academic research (CNRS, Inria) and industry (GSK, CEA), where he implemented CUDA/C++ real-time algorithms and delivered end-to-end ML solutions for imaging and genomics. He contributed to the geomstats open-source project, improving geodesic regression examples and fixing TensorFlow/PyTorch issues, reflecting strong practical knowledge of geometric ML. Trained in mathematical statistics at Cambridge and engineering at Ecole Centrale Paris, he blends rigorous theory with hands-on engineering to move advanced algorithms into production. Nicolas also completed entrepreneurship training at HEC Paris, signaling a founder’s appetite for product and business-driven innovation. He is particularly focused on translating complex manifold-based methods into reliable, clinically relevant imaging indicators that address real-world failures in assisted reproduction.
8 years of coding experience
1 year of employment as a software developer
Diplôme d'ingénieur, Mathematics, Diplôme d'ingénieur, Mathematics at Ecole Centrale ParisMaster's degree, Mathematical Statistics, Master's degree, Mathematical Statistics at University of CambridgeChallenge +, Innovation & Entrepreneuriat, Challenge +, Innovation & Entrepreneuriat at HEC Paris
Github Skills (7)
pytorch10
machine-learning10
tensorflow10
python10
geom9
geometry9
geo9
Programming languages (3)
Github contributions (5)
geomstats/geomstats
Sep 2019 - Nov 2022
Computations and statistics on manifolds with geometric structures.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:255 reviews, 1081 commits, 202 PRs in 3 years 2 months
Contributions summary:Nicolas's contributions primarily involved enhancements and fixes within the geomstats library, a project focused on computations and statistics on manifolds. The user made changes related to machine learning aspects within the library, including addressing issues, improving coverage, and adding geodesic regression examples. Furthermore, the user fixed issues related to computations with TensorFlow and PyTorch, demonstrating a working knowledge of those libraries.
gpu-programmingstatisticslie-groupsdeep-learningneural-networks
nguigs/geomstats
Sep 2019 - Mar 2023
Computations and statistics on manifolds with geometric structures.
Contributions:30 PRs, 746 pushes, 281 branches in 3 years 6 months
statisticsscientific-computingmanifoldsjuliageometric
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