Utility Maximisation in the Coordinator-Less IOTA Tangle. - Mathilde Jay; Ambre Mollard; Ye Sun; Ruyi Zheng; Isabel Amigo; Alexandre Reiffers-Masson; Santiago Ruano Rincón (UNet, 2021)

A Methodology and a Toolbox to Explore Dataset related to the Environmental Impact of HTTP Requests. - Christophe Cérin; Mathilde Jay; Laurent Lefèvre; Denis Trystram (IEEE Big Data, 2023)

An experimental comparison of software-based power meters: focus on CPU and GPU. - Mathilde Jay; Vladimir Ostapenco; Laurent Lefèvre; Denis Trystram; Anne-Cécile Orgerie; Benjamin Fichel (CCGrid, 2023)

A Versatile Methodology for Assessing the Electricity Consumption and Environmental Footprint of Machine Learning Training: from Supercomputers to Edge Devices. (Une méthodologie polyvalente pour évaluer la consommation électrique et l'empreinte environnementale de l'entraînement de l'apprentissage machine : des supercalculateurs aux équipements embarqués). - Mathilde Jay (2024)

Understanding the Environmental Impact of Generative AI Services. - Adrien Berthelot; Eddy Caron; Mathilde Jay; Laurent Lefèvre (Commun. ACM, 2025)