Jill-Jênn Vie

ML Researcher in RIKEN AIP, Tokyo, Japan

まだ世界は改変されていませんが、あとぐらいで変化します。
涼宮ハルヒの消失

Our paper Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing was accepted at AAAI 2019.
Our paper Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing was accepted at ICDM 2018.

I’m a researcher at RIKEN AIP working under the supervision of Hisashi Kashima in the Human Computation team. I’m interested in:

With Christoph Dürr, we wrote Efficient Programming with Python, a book containing 128 essential algorithms for preparing coding interviews & programming competitions (read the docs).
Currently available in French, Chinese and the English version is in press by Cambridge University Press.

My résumé: English / French
GitHub projects: jilljenn
vie@jill-jenn.net (check my piano videos 🎹)

Research Interests

Optimizing Human Learning ← Learning Analytics, Knowledge Tracing

How to use the logs of educational platforms (MOOCs, PIX) to optimize human learning?
Knowledge tracing machines (Vie and Kashima 2019) [tutorial], multidimensional item response theory, cognitive diagnosis [slides], multistage testing [slides] [code]

Representation Learning ← Feature Extraction

Using posters for manga and anime recommendations (Vie et al. 2017), deep factorization machines (Vie 2018), variational autoencoders

Interactive Machine Learning

Adaptive algorithms, optimal design, interactive recommender systems (like anime? try Mangaki)

Our article Knowledge Tracing Machines will be presented at AAAI 2019 in Hawaii. See also our tutorial.
Our article Knowledge Tracing Machines will be presented at AAAI 2019 in Hawaii. See also our tutorial.
Our paper Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario [arXiv] [slides] has been accepted to MANPU 2017 in Kyoto.
Our paper Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario [arXiv] [slides] has been accepted to MANPU 2017 in Kyoto.
Our article Automated Test Assembly using DPPs for Handling Learner Cold-Start in Large-Scale Assessments has been accepted in the journal IJAIED 2018.
Our article Automated Test Assembly using DPPs for Handling Learner Cold-Start in Large-Scale Assessments has been accepted in the journal IJAIED 2018.

Free software projects

Achievements

Teaching algorithms

2 books about algorithms & data structures (Chinese version out, English version in press)
1 Python package: pip install tryalgo128 algorithms for coding contests
✅ A data challenge with Kyoto University. [problem] [solutions]

… to everyone

✅ A programming summer school for K-12 girlsGirls Can Code! since 2014
✅ A TV show about algorithms that take control of our lives → Blame the Algorithm
✅ An open-source project: anime recommender systemMangaki

Publications

See my Scholar page

Books

I co-wrote two books about algorithms and data structures: preparing programming contests and coding interviews (Dürr and Vie 2016) and (Belghiti, Mansuy, and Vie 2016).
All 128 algorithms are implemented in Python on GitHub, in the package tryalgo.

Book Chapters

I wrote a chapter about adaptive assessment (Vie, Popineau, Bourda, et al. 2017) in a learning analytics book, where I identify similarities between cognitive diagnostic models and item response theory.

Conference Proceedings

Back in MSc, I was interested in cryptography and published an article about leakage resilience (Abdalla and Vie 2012).
We unified more educational data mining models in (Vie and Kashima 2019), and outperformed deep knowledge tracing in (Minn et al. 2018).


Abdalla, Michel, and Jill-Jênn Vie. 2012. “Leakage-Resilient Spatial Encryption.” In International Conference on Cryptology and Information Security in Latin America, 78–99. Springer.

Belghiti, Ismael, Roger Mansuy, and Jill-Jênn Vie. 2016. Les clés pour l’info : ENS et Agrégation (option D). Calvage et Mounet.

Dürr, Christoph, and Jill-Jênn Vie. 2016. Programmation efficace: Les 128 algorithmes qu’il faut avoir compris et codés dans sa vie. Ellipses.

———. 2018. 高效算法: 竞赛、应试与提高必修128例. 人民邮电出版社.

———. 2019. Efficient Programming with Python: 128 Essential Algorithms for Coding Test Prep. Cambridge University Press.

Minn, Sein, Yi Yu, Michel Desmarais, Feida Zhu, and Jill-Jênn Vie. 2018. “Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing.” In Proceedings of the 18th IEEE International Conference on Data Mining, to appear. https://arxiv.org/abs/1809.08713.

Vie, Jill-Jênn. 2018. “Deep Factorization Machines for Knowledge Tracing.” In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, 370–73. https://arxiv.org/abs/1805.00356.

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, to appear. https://arxiv.org/abs/1811.03388.

Vie, Jill-Jênn, Fabrice Popineau, Yolaine Bourda, and Éric Bruillard. 2016. “Adaptive Testing Using a General Diagnostic Model.” In European Conference on Technology Enhanced Learning, 331–39. Springer.

———. 2017. “A Review of Recent Advances in Adaptive Assessment.” In Learning Analytics: Fundaments, Applications, and Trends, 113–42. Springer.

Vie, Jill-Jênn, Fabrice Popineau, Éric Bruillard, and Yolaine Bourda. 2018a. “Automated Test Assembly for Handling Learner Cold-Start in Large-Scale Assessments.” International Journal of Artificial Intelligence in Education, 1–16. https://rdcu.be/G30H.

———. 2018b. “Utilisation de tests adaptatifs dans les MOOC dans un cadre de crowdsourcing.” Revue STICEF, Volume 24, numéro 2, 2017. https://doi.org/10.23709/sticef.24.2.6.

Vie, Jill-Jênn, Fabrice Popineau, Jean-Bastien Grill, Éric Bruillard, and Yolaine Bourda. 2015a. “Predicting Performance over Dichotomous Questions: Comparing Models for Large-Scale Adaptive Testing.” In 8th International Conference on Educational Data Mining.

———. 2015b. “Prédiction de performance sur des questions dichotomiques : comparaison de modèles pour des tests adaptatifs à grande échelle.” In Atelier Évaluation des Apprentissages et Environnements Informatiques.

Vie, Jill-Jênn, Fabrice Popineau, Françoise Tort, Benjamin Marteau, and Nathalie Denos. 2017. “A Heuristic Method for Large-Scale Cognitive-Diagnostic Computerized Adaptive Testing.” In Proceedings of the Fourth (2017) Acm Conference on Learning @ Scale, 323–26. ACM. https://github.com/jilljenn/las2017-wip/blob/master/poster-las2017.pdf.

Vie, Jill-Jênn, Florian Yger, Ryan Lahfa, Basile Clement, Kévin Cocchi, Thomas Chalumeau, and Hisashi Kashima. 2017. “Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario.” In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 03:21–26. https://arxiv.org/abs/1709.01584.