I am a third-year engineering student at ENSTA Paris, specializing in Artificial Intelligence. Alongside my engineering studies, I am pursuing the Master of Science in Mathematics, Vision, and Learning (MVA) at ENS Paris-Saclay — a highly selective, research-oriented program at the intersection of mathematics, machine learning, and computer vision.

Python, C++, SQL, Machine Learning, Deep Learning, Reinforcement Learning, Linear Algebra, Probability Theory, Optimization, Statistical Inference, Image Processing, Feature Extraction, Object Detection, 3D Reconstruction, Data Analysis, Feature Engineering, Model Evaluation, Visualization (Matplotlib, Seaborn), PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas, OpenCV, Linux, Git, VSCode, Jupyter, WSL, Problem Solving, Research, Scientific Writing, Team Collaboration

Aziz Ben‑Amira is a student and researcher at ENSTA Paris, located in Palaiseau in the Paris region of France. While pursuing his studies at ENSTA Paris, he focuses on data‑science projects, describing himself as a “tinkerer” in that field. Open to new opportunities, Aziz is actively seeking roles where he can apply his analytical skills and research experience. His professional profile highlights a blend of academic training and hands‑on data‑science experimentation, positioning him as a versatile candidate for data‑driven positions.

Data science projects, data analysis, research collaborations, software development and mlops, programming, Git/development workflows, hands-on tinkering/experimentation, open to new opportunities

Projects: Data‑science experiments — feature engineering, model prototyping and evaluation implemented in Python ; MLOps prototypes — reproducible training/evaluation pipelines and CI tied to Git workflows; research code and demos hosted on GitHub (Aziz-Benamira) supporting ENSTA Paris assignments.

I am Achraf Chaouch, a final-year AI engineering student at ENSTA Paris, passionate about computer vision, deep learning, and AI applications in sports analytics. I have completed internships in research and industry settings, including INRIA Saclay, 2BX, and CEA-List’s Computer Vision Lab, where I gained hands-on experience in multi-view re-identification, 3D reconstruction, and feature learning. I am motivated to contribute to innovative AI projects and explore how advanced machine learning can transform real-world applications.

I am keen to explore AI applied to sports analytics, particularly the use of computer vision to analyze multiple sports beyond football. I am also interested in multi-modal learning, 3D reconstruction, and real-time analytics, and I hope to connect with professionals and researchers working on cutting-edge AI applications in these areas.

Currently, I am exploring sports analytics using computer vision, focusing on multi-view object and player tracking, 3D reconstruction from video data, and performance metrics extraction. I am also experimenting with deep learning pipelines for automated event detection and pose estimation in team sports, aiming to translate research methods into actionable insights.