Jill-jênn Vie
Lecturer at Inria
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolJill-jênn Vie is a machine learning researcher and lecturer in Paris with 13 years of experience designing adaptive and interactive systems that predict student performance and optimize human learning. She blends academic rigor—PhD work on adaptive testing and roles at Inria, École Polytechnique and RIKEN—with practical engineering, contributing to open-source projects such as a Python typesetting engine in the well-known 500lines collection and deep learning notebooks for seq2seq models. Passionate about algorithmic problem solving and scaling computer science education for children, she has co-authored two books and helps shape policy around AI in education through past work with the European Commission. Her work sits at the intersection of pedagogy, privacy-aware ML (including federated learning concerns), and hands-on software craftsmanship—fixing the world one commit at a time.
13 years of coding experience
3 years of employment as a software developer
Bachelor of Science (BSc), Computer Science, Bachelor of Science (BSc), Computer Science at Ecole normale supérieure de LyonMaster of Science (MSc), Computer Science, Master of Science (MSc), Computer Science at École Normale Supérieure de Cachan
English, Japanese, French
Stackoverflow
1,841reputation
275kreached
42answers
4questions
jsonp
top-5%
json
top-5%
Github Skills (36)
algorithm10
algorithms10
pytorch10
python10
machine-learning10
typesetting10
sequence-to-sequence10
data-structure9
json9
attention-mechanism9
jsonp9
computer-engineering9
deep-learning9
data-structures9
file-processing8
Programming languages (18)
Github contributions (5)
dataflowr/notebooks
Oct 2020 - Dec 2021
code for deep learning courses
Role in this project:
ML Engineer
ML EngineerContributions:16 commits, 5 PRs, 15 pushes in 1 year 2 months
Contributions summary:Jill-jênn contributed to the development of deep learning models, specifically focusing on sequence-to-sequence models, as evidenced by the addition of `seq2seq.py` and its associated components, including an `EncoderRNN`, `DecoderRNN`, and `AttnDecoderRNN`. The commits involve constructing and training a seq2seq model, incorporating techniques like teacher forcing and attention mechanisms. Furthermore, the user's efforts are related to a Federated Poisoning project, demonstrating their engagement in advanced machine learning methodologies and a focus on dataset and model manipulation.
deep-learningpytorchmachine-learning
aosabook/500lines
Jun 2014 - Nov 2014
500 Lines or Less
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:12 commits in 5 months
Contributions summary:Jill-jênn implemented and refined a typesetting engine, the core of which is written in Python, that determines optimal breakpoints for text justification. They added functionality to generate PostScript output for rendering the formatted text. Furthermore, the user refactored the code for improved style and readability, adhering to PEP 8 guidelines, and introduced a class to streamline the typesetting process.
linesmodular-designlistsjava
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