Amine Nabli

At Maison des Sciences Numériques, I processed image and spectral data for a battery quality assessment digital twin. I implemented machine learning and deep learning models for computer vision and signal analysis.

I maintained 79% classification accuracy while reducing the feature space by 86%. I also created visualizations of preprocessed data and spectral signals.

On my Enterprise Text-to-SQL Reasoning Engine project, I prepared stratified datasets and fine-tuned language models with an MLOps pipeline. The work included curriculum learning, semantic validation, and evaluation.

I also developed projects in healthcare intelligence, texture classification, dermatological diagnosis, click-through rate prediction, and pothole detection. I hold a Master’s degree in Data Exploration and Decision Support and am interested in data analysis, automation, and AI in an R&D team.