Amine Bendahmane
Software Engineer at Dassault Systèmes
Lille, Hauts-de-France
Summary
👤
Senior🎓
Top SchoolAmine Bendahmane is a software engineer with a PhD in AI & Robotics and over a decade of hands-on experience spanning R&D, deep learning, robotics, teaching, and technology program management. He has led technology delivery for major sporting events (including Paris 2024 and the Mediterranean Games) and now contributes to product engineering at Dassault Systèmes. Technically fluent in Python, C++, ROS and TensorFlow/Keras, he has built practical ML systems such as a CNN-based facial expression recognizer that fuses face landmarks and video-stream prediction. As a former university teacher and frequent workshop presenter, he blends rigorous academic training with clear technical communication and mentorship. He also volunteers in non-profit education initiatives, demonstrating a sustained commitment to community-focused tech outreach.
10 years of coding experience
7 years of employment as a software developer
PhD, PhD at University of Sciences and Technology of Oran Mohamed Boudiaf (USTOMB)Bachelor's degree, Bachelor's degree at Sciences and Technology University of Oran - Mohamed Boudiaf (USTO-MB)
Arabic, English, French
Github Skills (8)
mask-rcnn10
faster-rcnn10
machine-learning10
opencv10
deep-learning10
tensorflow10
python10
image-classification9
Programming languages (5)
Github contributions (5)
amineHorseman/facial-expression-recognition-using-cnn
Sep 2016 - Mar 2019
Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream
Role in this project:
ML Engineer
ML EngineerContributions:44 commits, 1 PR, 9 pushes in 2 years 7 months
Contributions summary:Amine primarily contributed to the development and improvement of a deep learning model for facial expression recognition. They modified the model's architecture by incorporating face landmarks as a second input and merging it with the final fully connected layer. Further enhancements included the addition of a learning rate hyperparameter and its decay. The user also implemented evaluation metrics and added the capability to predict emotions from video streams.
tensorflowcnncnn-classificationmachine-learningdeep-learning
amineHorseman/my-talks
Oct 2018 - Oct 2022
Contributions:19 commits, 6 pushes in 4 years 1 month
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