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sylvaincom/README.md

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, called skore, 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.

Contact: sylvain.combettes8 [a t] gmail.com

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

    The scikit-learn Modeling Companion

    Python 115 8

  2. astride astride Public

    [EUSIPCO 2024] Python implementation of "ASTRIDE: Adaptive Symbolization for Time Series Databases"

    Jupyter Notebook 16 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 9

  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 13 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 23 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 11