Jeremy Cohen
Researcher at cnrs
Villeurbanne, Auvergne-Rhône-Alpes, France
Summary
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RockstarJeremy Cohen is a researcher based in Villeurbanne, France with 8 years’ experience in signal processing and data mining, specializing in tensor decomposition techniques. Currently at CNRS, he focuses on making tensor methods practical for real-world datasets, evidenced by contributions to the widely used TensorLy library where he improved dataset loading, testing, and documentation for benchmarks like Indian Pines and Kinetic. Jeremy blends rigorous research with hands‑on data engineering to ensure reproducible experiments and accessible APIs. Known for attention to dataset integrity and tooling, he helps bridge the gap between algorithmic innovation and reproducible, usable software.
8 years of coding experience
Github Skills (9)
machine-learning10
tensor10
python10
data-management10
numpy10
datasets10
data-set10
scipy9
documentation7
Programming languages (4)
Github contributions (5)
tensorly/tensorly
Jun 2020 - Jan 2023
TensorLy: Tensor Learning in Python.
Role in this project:
Data Scientist
Data ScientistContributions:34 reviews, 12 commits, 12 PRs in 2 years 7 months
Contributions summary:Jeremy primarily contributed to the dataset loading and management aspects of the tensorly library. Their commits focused on adding and updating datasets like Indian Pines and Kinetic, involving changes to data import functions and tests. They also addressed formatting issues and updated documentation, specifically for the dataset module, including the API references. These contributions suggest a focus on preparing and integrating data for use within the library's tensor-based machine learning functionalities.
tensor-factorizationtensor-decompositionpythontensor-learningtensor
cohenjer/dlra
Nov 2021 - Sep 2024
Contributions:1 release, 2 pushes, 1 branch in 2 years 9 months
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