À propos de Ali
I specialize in digital signal processing and machine learning for digital instrumentation, with hands-on experience across the entire project lifecycle. This includes everything from configuring data acquisition systems, processing raw data, and developing machine learning models, to training and validating them for specific applications. I am also skilled in integrating these solutions into embedded systems for real-time inference, ensuring that the technology is both effective and scalable. With a strong foundation in both theory and practical implementation, I aim to drive innovation in digital instrumentation and embedded machine learning.
Arabe
Bilingue ou natif
Français
Capacité professionnelle complète
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- UpworkSignal Processing Freelancerjanvier 2025 - juillet 2025 (6 mois)• Develop a reliable method for authenticating coins.– Analyze the signals to create a profile for each coin.– Optimize the code to run in real time.
- ACOEMMachine Learning R&D Engineerfévrier 2024 - Aujourd'hui (2 ans et 4 mois)France• Analyze vibration data (digital filters, transfer functions).• Develop unsupervised ML technique for anomaly detection in rotating machines.– Reduce the load of service team.• Develop automated diagnostic system for rotating machines.– Implement weakly/self-supervised models.– Develop smart RPM system for engine speed measurement.– Improve the accuracy by 10%• Fine-tune Deep learning model for acoustic source classification.• Fine-tune speech language models for speech identification.• Create speech recognition system for predictive maintenance.• Refactor and optimize the code for poduction use.
- CEAPh.D Embedded ML for Nuclear Instrumentationjanvier 2021 - janvier 2024 (3 ans)• Build framework for data acquisition and labeling.• Review supervised and self-supervised ML for nuclear applications.• Compare deep learning models with state-of-the-art methods.• Develop a novel discrimination method (patent).• Review ML implementation on FPGA (MLP, 1D-CNN).• Implement MLP, 1D-CNN and deterministic methods on FPGA.
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Formations
- Ph.D. in Signal Processing and Embedded Machine LearningParis Saclay University2024Ph.D. in Signal Processing and Embedded Machine Learning
- M.Sc. in Optimization and Machine Learning for Complex SystemsUniversity of Technology of Compiegne2020M.Sc. in Optimization and Machine Learning for Complex Systems