About Anouar
French
Native or bilingual
English
Fluent
Arabic
Native or bilingual
Experience
- ThalesAI Applied research scientistJanuary 2022 - Today (4 years and 6 months)Meudon, FranceDeveloped and optimized biometric algorithms, ensuring they met predefined performance targets during rigorous internal and ex‑ternal evaluationsImplemented, developed, and integrated advanced algorithms into various internal and external deliverables, including SoftwareDevelopment Kits, software system solutions, and hardware products.Established scalable AI pipelines: Automated data preprocessing, model training, and evaluation workflows, leveraging PyTorch andTensorFlow for production‑grade model deliverySupported, tested, and troubleshooted algorithms for existing and new technologies and products, ensuring high accuracy and speedin biometric identification
- Nokia Bell LabsPhD student/Research engineerTELECOMMUNICATIONSOctober 2018 - October 2021 (3 years)Massy, FranceExplored the potential of Graph Neural Networks (GNNs) and deep reinforcement learning in addressing the resource allocation problem in 5G network slicing.- Unveiled the capability of GNNs and deep reinforcement learning in solving resource allocation challenges within 5G network slicing.- Implemented a reinforcement learning-based strategy to enhance the performance of heuristics, focusing on gap optimality, for the resource allocation problem in 5G network slicing.- Proposed a machine learning-based solution for monitoring 5G network slices, enabling efficient management of resources.- Presented a novel learning approach combining graph neural network models and genetic algorithms to determine the optimal monitoring of network slices with probing cycles in 5G.- Investigated the application of deep reinforcement learning, graph neural networks, and combinatorial optimization in the context of 5G network slicing.- Contributed to advancing the understanding of optimized resource management techniques and their application in future 5G networks.
- I3SDeep/Machine learningHEALTH AND WELLNESSMarch 2018 - August 2018 (6 months)Nice, France- Developed and trained machine and deep learning algorithms on electronic health records (EHR) for improved performance.- Implemented strategies to parallelize the algorithms on a Jetson TX2 cluster, optimizing for low-power embedded modules within hospitals.- Balanced model precision, execution time, and memory usage, achieving a trade-off that met the unique requirements of the healthcare environment and reduce the energy consumption of training and inference.- Utilized TensorFlow, scikit-learn, and Keras to optimize and fine-tune the learning models.- Leveraged distributed and parallel deep learning techniques to process and analyze large volumes of medical data efficiently.- Employed NVIDIA Jetson TX2 and CUDA to accelerate the training process and enhance overall performance.- Successfully achieved a compromise between accuracy, execution time, and memory usage for practical deployment in hospitals.
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Education
- Master Computer scienceUniversité Nice Sophia Antipolis2018
- Ingénieur en Machine learningInstitut National des postes et télecommunications2018
Certifications
- Machine learningCoursera2017
- Deep LearningCoursera2017