Jill-Jênn Vie

Researcher at Inria

Clés Python

I am a research scientist at Inria, Soda team (hiring), interested in factorization, generative models and educational applications of machine learning. I enjoy teaching deep learning at ENS and competitive programming at X & ENS Paris-Saclay.

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

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 @ AIED 2020 [video].

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
✅ Worked for: European Commission, EDM 2021
✅ See my projects and CV

Responsibilities

Selected Publications

See all publications / My Scholar page

Bergner, Yoav, Peter Halpin, and Jill-Jênn Vie. 2022. “Multidimensional Item Response Theory in the Style of Collaborative Filtering.” Psychometrika 87 (1): 266–88. https://rdcu.be/cAcgu.
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.
Vassoyan, Jean, Jill-Jênn Vie, and Pirmin Lemberger. 2023. “Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning.” In Proceedings of the Sixteenth International Conference on Educational Data Mining (EDM 2023), in press. 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.
———. 2023. “Deep Knowledge Tracing Is an Implicit Dynamic Multidimensional Item Response Theory Model.” In Proceedings of the 31st International Conference on Computers in Education, in press. https://inria.hal.science/hal-04180391.
Vie, Jill-Jênn, Tomas Rigaux, and Hisashi Kashima. 2022. “Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems.” In 2022 IEEE International Conference on Big Data (Big Data), 5607–14. Los Alamitos, CA, USA: IEEE Computer Society. https://jiji.cat/bigdata/vie2022vfm.pdf.
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.