Jill-Jênn Vie

Researcher at Inria, Lecturer at X

Competitive Programming in Python

I am a research scientist at Inria, Soda team and a lecturer at École polytechnique and ENS Paris where I teach competitive programming and deep learning. My research interests are recommender systems, generative models and AI in education (Kandemir et al. 2024), notably using reinforcement learning (Vassoyan et al. 2024).

We are hiring engineers, postdocs and PhD students, as we were awarded a IA-Cluster chair about ATLAS: AI for teaching and learning at scale at Paris-Saclay University & an ANR JCJC grant. 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) or X.

Research Interests

Knowledge tracing

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*, Rigaux*, and Minn 2022).

Collaborative filtering

We study applications of collaborative filtering for education (~ multidimensional item response theory), drug repurposing (with Clémence Réda) and diversity of recommendations (pass Culture, Kyoto U).
Our paper Diversified recommendations of cultural activities with personalized DPPs [slides] has been accepted to RecSys workshop RecSoGood 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).

Other Achievements

✅ I worked on the government project Pix. This is free software that certifies the digital skills of all French citizens
✅ We founded the Girls Can Code! schools for K-12 girls running since 2014
✅ See my projects and CV

Highest Peaks where I’ve been

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.” In Proceedings of 9th Educational Data Mining in Computer Science Education Workshop (CSEDM 2025). Palermo, Italy. https://hal.science/hal-05089333.
Choffin, Benoı̂t, Fabrice Popineau, Yolaine Bourda, and Jill-Jênn Vie. 2019. “DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills.” In 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.” In Proceedings of RecSoGood workshop at RecSys 2025, in press. Prague, Czech Republic.
Kandemir, Erva Nihan, Jill-Jênn Vie, Adam Sanchez-Ayte, Olivier Palombi, and Franck Ramus. 2024. “Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students’ Training Data.” In Proceedings of the Fourteenth International Conference on Learning Analytics and Knowledge (LAK 2024), in press. https://arxiv.org/abs/2403.07908.
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.
———. 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.” In Proceedings of the 33th AAAI Conference on Artificial Intelligence, 750–57. https://arxiv.org/abs/1811.03388.
Vie*, Jill-Jênn, Tomas Rigaux*, and Sein Minn. 2022. “Privacy-Preserving Synthetic Educational Data Generation.” In 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.