Dorian Joubaud

CycleGAN-based data augmentation pipeline for Remaining Useful Life (RUL) prediction under unsupervised domain adaptation, improving generalization across domains using adversarial learning and correlation alignment.

BALANCER is a machine learning framework that recommends the best data augmentation techniques for imbalanced time series classification tasks, validated over 720 datasets and explained with XAI tools.

PhD student in Machine Learning at the University of Luxembourg’s SnT, focusing on data augmentation for time-series analysis in Industry 4.0.

Master's degree from Institut Polytechnique de Paris at the University Paris-Saclay, in partnership with Telecom SudParis, ENSIIE, and the University of Versailles, in agreement with CNAM. Information Processing and Data Exploitation, leading to the profession of Data Scientist as well as the field of Big Data, and which can lead to research.

Engineering student at ENSIIE, specializing in Data Science and Artificial Intelligence, with a focus on machine learning and data analysis.