Charles Ollion
Co-Founder & CTO at Self-employed
Auvergne-Rhône-Alpes, France
Summary
🤩
Rockstar🎓
Top SchoolCharles Ollion is a data scientist and engineering leader with 11 years of experience who co-founded NAIA Science and now serves as its CTO, building numerical tools for pollution and biodiversity assessment. He combines deep learning research (PhD-level training in stochastic optimization) with practical product delivery from prior CTO roles and independent consulting, including contributions to environmental projects like Plastic Origins. An experienced educator, he taught M2 deep learning courses and authored widely-used lectures and labs on CNNs, RNNs and anomaly detection for Institut Polytechnique de Paris. Charles bridges academia and industry through hybrid deep learning research at CMAP and hands-on engineering, and maintains an active open-source footprint that aids reproducible ML education.
11 years of coding experience
14 years of employment as a software developer
Master of Science (M.Sc.) Computer Science, Master of Science (M.Sc.) Computer Science at KTH Royal Institute of TechnologyEngineer's degree Computer Science, Engineer's degree Computer Science at Télécom ParisDoctor of Philosophy (PhD) Computer Science - Stochastic Optimization, Doctor of Philosophy (PhD) Computer Science - Stochastic Optimization at Pierre and Marie Curie University
French, English
Github Skills (8)
mask-rcnn10
faster-rcnn10
machine-learning10
jupyter-notebook10
computer-vision10
deep-learning10
python9
neural-network9
Programming languages (6)
Github contributions (5)
m2dsupsdlclass/lectures-labs
Feb 2017 - Mar 2022
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:4 releases, 162 commits, 14 PRs in 5 years 1 month
Contributions summary:Charles contributed to the Deep Learning lectures by providing slides on Convolutional Neural Networks (CNNs), including sections on localization, segmentation, and object detection. They also added content regarding Recurrent Neural Networks (RNNs) for language modelling and discussed techniques for image captioning, focusing on different models. Additionally, the user created exercises around analyzing and visualizing latent space for anomaly detection.
pythonscienceslidedeep-learningjupyter-notebook
charlesollion/surfnet-android
May 2023 - Nov 2024
Android version of Surfnet using tflite
Contributions:32 reviews, 36 PRs, 127 pushes in 1 year 6 months
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