Loop Aggregation for Approximate Scientific Computing. - June Sallou; Alexandre Gauvain; Johann Bourcier; Benoît Combemale; Jean-Raynald de Dreuzy (ICCS (2), 2020)

When Scientific Software Meets Software Engineering. - Dorian Leroy; June Sallou; Johann Bourcier; Benoît Combemale (Computer, 2021)

A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems. - Benoît Combemale; Jörg Kienzle; Gunter Mussbacher; Hyacinth Ali; Daniel Amyot; Mojtaba Bagherzadeh; Edouard Batot; Nelly Bencomo; Benjamin Benni; Jean-Michel Bruel; Jordi Cabot; Betty H. C. Cheng; et al. (IEEE Softw., 2021)

Data-Centric Green AI An Exploratory Empirical Study. - Roberto Verdecchia; Luís Cruz 002; June Sallou; Michelle P. Lin; James Wickenden; Estelle Hotellier (ICT4S, 2022)

On the Role of Computer Languages in Scientific Computing. - Dorian Leroy; June Sallou; Johann Bourcier; Benoît Combemale (Comput. Sci. Eng., 2022)

On reliability and flexibility of scientific software in environmental science: towards a systematic approach to support decision-making. (Agir sur la fiabilité et la flexibilité des logiciels scientifiques en sciences de l'environnement: vers une approche systématique d'aide à la prise de décision). - June Sallou (2022)

Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI. - Tim Yarally; Luis Cruz 002; Daniel Feitosa; June Sallou; Arie van Deursen (CAIN, 2023)

The Two Faces of AI in Green Mobile Computing: A Literature Review. - Wander Siemers; June Sallou; Luís Cruz 002 (SEAA, 2023)

Batching for Green AI - An Exploratory Study on Inference. - Tim Yarally; Luís Cruz 002; Daniel Feitosa; June Sallou; Arie van Deursen (SEAA, 2023)

Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques. - Lorena Poenaru-Olaru; June Sallou; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (GREENS@ICSE, 2023)

Preface/Organization. - Irit Hadar; June Sallou (ICT4S (Doctoral Symposium, Demos, Posters), 2023)

EnergiBridge: Empowering Software Sustainability through Cross-Platform Energy Measurement. - June Sallou; Luis Cruz 002; Thomas Durieux (CoRR, 2023)

A systematic review of Green AI. - Roberto Verdecchia; June Sallou; Luis Cruz 002 (WIREs Data. Mining. Knowl. Discov., 2023)

Material of the paper entitled "Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI". - Tim Yarally; Luís Cruz 002; Daniel Feitosa; June Sallou; Arie van Deursen (Zenodo, 2023)

Green AI in Action: Strategic Model Selection for Ensembles in Production. - Nienke Nijkamp; June Sallou; Niels van der Heijden; Luís Cruz 002 (AIware, 2024)

Breaking the Silence: the Threats of Using LLMs in Software Engineering. - June Sallou; Thomas Durieux; Annibale Panichella (NIER@ICSE, 2024)

Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations. - Pooja Rani 001; Jan-Andrea Bard; June Sallou; Alexander Boll; Timo Kehrer; Alberto Bacchelli (CoRR, 2025)

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices. - Luis Cruz 002; João Paulo Fernandes; Maja H. Kirkeby; Silverio Martínez-Fernández; June Sallou; Hina Anwar; Enrique Barba Roque; Justus Bogner; Joel Castaño; Fernando Castor; Aadil Chasmawala; Simão Cunha; et al. (CoRR, 2025)

Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy. - Lorena Poenaru-Olaru; June Sallou; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (CoRR, 2025)