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 where I teach competitive programming. My research interests are recommender systems, generative models and educational applications of machine learning (Kandemir et al. 2024), notably using RL (Vassoyan, Vie, and Lemberger 2023).

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

Research Interests

Deep generative models of time series

If we can generate assessment data from MOOCs, we can predict, explain and optimize human learning.
If we can do this with privacy guarantees, we can share this dataset with more users. (Vie*, Rigaux*, and Minn 2022)
See our tutorial about knowledge tracing and our optimizing human learning workshop @ LAK 2024.

Recommender systems with side information

How to model uncertainty and side information in preference elicitation? See our demo Mangaki and keynote.

Our article Knowledge Tracing Machines has been presented at AAAI 2019. See also our code & tutorial (Vie and Kashima 2019).
Our paper Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario [slides] has been accepted to MANPU 2017.
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

Responsibilities

Collaborators

Selected Publications

See all publications / My Scholar page

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.
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.
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.
Vassoyan, Jean, Jill-Jênn Vie, and Pirmin Lemberger. 2023. “Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning.” In Proceedings of the 16th International Conference on Educational Data Mining, EDM 2023, Bengaluru, India, July 11-14, 2023, edited by Mingyu Feng, Tanja Käser, Partha P. Talukdar, Rakesh Agrawal, Y. Narahari, and Mykola Pechenizkiy. International Educational Data Mining Society. https://arxiv.org/abs/2305.06398.
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.