Jill-Jênn Vie

Researcher at Inria, Lecturer at l’X & ENS

Competitive Programming in Python

My research interests are AI applications to education. I train École polytechnique for competitive programming at ICPC and I teach foundations of deep learning at ENS Paris.

We are hiring engineers 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.
Stay tuned for job offers, 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 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

Mount Fuji, 3776 m

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 Nihan, Jill-Jênn Vie, Adam Sanchez-Ayte, Olivier Palombi, and Franck Ramus. 2026. “Investigating the Influence of Training Difficulty on the Learning Outcomes of Medical Students.” Journal of Computer Assisted Learning 42 (1): e70172. https://doi.org/https://doi.org/10.1002/jcal.70172.
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.
Nagai, Ryosuke, Kyohei Atarashi, Koh Takeuchi, Jill-Jênn Vie, and Hisashi Kashima. 2026. “Estimating Learners’ Skill Acquisition Without Temporal Information.” The 27th International Conference on AI in Education, Seoul, South Korea.
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.