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Achievement: QuickdrawAchievement: Pull Sharkx2Achievement: Pair ExtraordinaireAchievement: StarstruckAchievement: Arctic Code Vault Contributor

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sylvaincom/README.md
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Hi there, I'm Sylvain Combettes 👋

Since September 2024, I have been a Machine Learning Product Engineer at :probabl., the official brand operator of scikit-learn. I contribute to designing and developing a data scientist companion to empower data scientists and companies in mastering their entire data lifecycle. Feel free to reach out to me if you are interested in such a product!

Previously, I was a PhD student, at the Centre Borelli research lab from Ecole Normale Supérieure Paris-Saclay, where I worked on machine learning applied to time series, under the supervision of Laurent Oudre and Charles Truong. More precisely, my research focused on symbolic representation for time series, as well as distance measures on them.

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  1. probabl-ai/skore probabl-ai/skore Public

    Skore lets you "Own Your Data Science." It provides a user-friendly interface to track and visualize your modeling results, and perform evaluation of your machine learning models with scikit-learn.

    Python 70 7

  2. astride astride Public

    [Preprint] Python implementation of "ASTRIDE: Adaptive Symbolization for Time Series Databases"

    Jupyter Notebook 13 3

  3. d-symb d-symb Public

    [ICDMW 2023] Python implementation of d_{symb}: "An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals"

    Jupyter Notebook 6

  4. boniolp/dsymb-playground boniolp/dsymb-playground Public

    [ICDE 2024] Python and Streamlit implementation of "d_{symb} playground: an interactive tool to explore large multivariate time series datasets"

    Python 10 2

  5. medgan-tips medgan-tips Public

    [Python] Additional works on Edward Choi's medGAN (generative adversarial network for electronic health records). In particular: boosting the prediction score using dataset augmentation.

    Jupyter Notebook 21 4

  6. comparison-distributions comparison-distributions Public

    [Python] Comparison of empirical probability distributions. Integral probability metrics (e.g. Kantorovich metric). f-divergences (e.g. Kullback-Leibler). Application to the Choquet integral.

    Jupyter Notebook 10