I am a PhD student in Computer Science at LIP6 laboratory. My research focuses on fairness in AI applied to education (human learning). I studied AI at Paris-Saclay University where Isabelle Guyon was one of my professor and I did my research internship under the supervision of Hugo Jair Escalante, Isabelle Guyon and Marc Evrard. Prior to this, I was graduated as an engineer from Arts et Métiers (ENSAM).
Publication
2023: [workshop paper] Mélina Verger, Chunyang Fan, Sébastien Lallé, François Bouchet, Vanda Luengo. A Fair Post-Processing Method based on the MADD Metric for Predictive Student Models. 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education (RKDE 2023) at ECML PKDD 2023, September 2023, Turino, Italy.
2023: [conference paper] Mélina Verger, Sébastien Lallé, François Bouchet, Vanda Luengo. Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models. Sixteenth International Conference on Educational Data Mining (EDM 2023), July 2023, Bangalore, India.
2023: [conference paper] Mélina Verger, François Bouchet, Sébastien Lallé, Vanda Luengo. Caractérisation et mesure des comportements discriminants des modèles prédictifs. 11ème Conférence sur les Environnements Informatiques pour l'Apprentissage Humain (EIAH 2023), June 2023, Brest, France. hal-04131676
2022: [conference paper] Mélina Verger. Investiguer la notion d'équité algorithmique dans les environnements informatiques pour l'apprentissage humain. 9ièmes RJC EIAH 2022 : Environnements Informatiques pour l’Apprentissage Humain, May 2022, Lille, France. hal-03678739
2021: [report] Mélina Verger, Hugo Jair Escalante. Predicting students’ performance in online courses using multiple data sources. DOI: 10.48550/arXiv.2109.07903. The [code] and a [Codalab challenge] are available.