Jill-Jênn Vie

Researcher at Inria, Lecturer at l’X

Competitive Programming in Python

I am a researcher at Inria, Soda team and a lecturer at École polytechnique and ENS Paris where I teach competitive programming (Polytechnique won ICPC SWERC 2024 🥇🏆) and deep learning. My research interests are recommender systems and application of machine learning and reinforcement learning to education (Vassoyan et al. 2024).

We are hiring engineers, postdocs and PhD students, as we were awarded an IA-Cluster chair about ATLAS: AI for teaching and learning at scale at Paris-Saclay University & an ANR JCJC grant that I can share with interested people. You can either wait for a more precise job offer description, or directly reach out with a CV.

Feel free to contact me by mail (PGP key).

Research Interests

Knowledge tracing: predicting student performance

Training a student model on student data can be used to optimize teaching using reinforcement learning.
I am also interested in synthetic tabular data generation (Vie* et al. 2022).

Recommender systems: collaborative filtering

We study applications of collaborative filtering for education, personalized medicine (Réda et al. 2025a, 2025b) and diversity of recommendations using determinantal point processes (pass Culture, Kyoto U) [slides] (Ibrahim et al. 2025).

Our article Knowledge Tracing Machines has been presented at AAAI 2019. See also our code & tutorial (Vie and Kashima 2019).
We received the Best Paper Award at EDM 2019 for our learning/forgetting student model DAS3H (Choffin et al. 2019).

Highest Peaks where I’ve been

See my projects and CV

Government & State Startups

Programming

Abroad

Collaborators

Selected Publications

See all publications / My Scholar page

Agrawal, Anav, and Jill-Jênn Vie. 2025. “AlgoAce: Retrieval-Augmented Generation for Assistance in Competitive Programming.” Proceedings of 9th Educational Data Mining in Computer Science Education Workshop (CSEDM 2025) (Palermo, Italy), July. https://hal.science/hal-05089333.
Choffin, Benoît, Fabrice Popineau, Yolaine Bourda, and Jill-Jênn Vie. 2019. “DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills.” Proceedings of the Twelfth International Conference on Educational Data Mining (EDM 2019), 29–38. https://arxiv.org/abs/1905.06873.
Ibrahim, Carole, Hiba Bederina, Daniel Cuesta, Laurent Montier, Cyrille Delabre, and Jill-Jênn Vie. 2025. “Diversified recommendations of cultural activities with personalized determinantal point processes.” Proceedings of RecSoGood workshop at RecSys 2025 (Prague, Czech Republic), September, in press.
Kandemir, Erva, Jill-Jênn Vie, Adam Sanchez-Ayte, Olivier Palombi, and Franck Ramus. 2025. “Investigating the Influence of Training Difficulty on the Learning Outcomes of Medical Students.” Journal of Computer Assisted Learning, in press. https://inria.hal.science/hal-05371759v1.
Kita, Naoyuki, Jill-Jênn Vie, Koh Takeuchi, and Hisashi Kashima. 2026. “Robust Post-Hoc Score Allocation in Exams.” The 16th International Learning Analytics & Knowledge Conference, Bergen, Norway.
Réda, Clémence, Jill-Jênn Vie, and Olaf Wolkenhauer. 2025a. “Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing.” Scientific Reports 15 (2711): 2711. https://doi.org/10.1038/s41598-025-85927-x.
Réda, Clémence, Jill-Jênn Vie, and Olaf Wolkenhauer. 2025b. “Joint Embedding-Classifier Learning for Interpretable Collaborative Filtering.” BMC Bioinformatics 26 (1): 26. https://doi.org/10.1186/s12859-024-06026-8.
Vassoyan, Jean, Anan Schütt, Jill-Jênn Vie, Arun-Balajiee Lekshmi-Narayanan, Elisabeth André, and Nicolas Vayatis. 2024. “A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents.” https://inria.hal.science/hal-04779162.
Vie, Jill-Jênn, and Hisashi Kashima. 2019. “Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing.” Proceedings of the 33th AAAI Conference on Artificial Intelligence, 750–57. https://arxiv.org/abs/1811.03388.
Vie, Jill-Jênn, Fabrice Popineau, Françoise Tort, Benjamin Marteau, and Nathalie Denos. 2017. “A Heuristic Method for Large-Scale Cognitive-Diagnostic Computerized Adaptive Testing.” Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, 323–26. https://github.com/jilljenn/las2017-wip/.
Vie*, Jill-Jênn, Tomas Rigaux*, and Sein Minn. 2022. “Privacy-Preserving Synthetic Educational Data Generation.” Proceedings of EC-TEL 2022. Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption, 393–406. https://hal.archives-ouvertes.fr/hal-03715416.