Peer-reviewed publications in journals

  1. Longitudinal brain structure changes in Parkinson’s disease: a replication study
    Andrzej Sokolowski, Nikhil Bhagwat, Yohan Chatelain, Mathieu Dugre, Alexandru Hanganu, Oury Monchi, Brent McPherson, Michelle Wang, Jean-Baptiste Poline, Madeleine Sharp, Tristan Glatard. In PLOS ONE (2024)
  2. Numerical Stability of DeepGOPlus Inference
    Ines Gonzalez Pepe, Yohan Chatelain, Gregory Kiar, Tristan Glatard. In PLOS ONE (2024)
  3. PyTracer: Automatically profiling numerical instabilities in Python.
    Yohan Chatelain, Nigel Yong, Gregory Kiar, Tristan Glatard. IEEE Transactions on Computers (IEEE TC) (2022)
  4. Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics.
    Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard. In Neurons, Behavior, Data Analysis and Theory, 2021.
  5. Numerical Uncertainty in Analytical Pipelines Lead to Impactful Variability in Brain Networks.
    Gregory Kiar, Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, Ariel Rokem, Gaël Varoquaux, Bratislav Misic, Alan C. Evans, Tristan Glatard. In PLOS ONE (2021).
  6. Piecewise holistic autotuning of parallel programs with CERE.
    Mihail Popov, Chadi Akel, Yohan Chatelain, William Jalby, and Pablo de Oliveira Castro. Concurrency and Computation: Practice and Experience, vol. 29, Aug 2017.

Peer-reviewed publications in conferences

  1. Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis.
    Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Hae Lang Park, Yohan Chatelain, Tristan Glatard Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE, MICCAI) (2023)
  2. Reproducibility of tumor segmentation outcomes with a deep learning model.
    Morgane Des Ligneris, Axel Bonnet, Yohan Chatelain, Tristan Glatard, Michaël Sdika, Gaël Vila, Valentine Wargnier-Dauchelle, Sorina Pop, Carole Frindel. International Symposium on Biomedical Imaging (ISBI), 2023.
  3. Reducing numerical precision preserves classification accuracy in Mondrian Forests.
    Marc Vicuna, Martin Khannouz, Gregory Kiar, Yohan Chatelain, Tristan Glatard. 6th Workshop on Real-time Stream Analytics, Stream Mining, CER/CEP & Stream Data Management In 2021 IEEE International Conference on Big Data (Big Data) (pp. 2785-2790).
  4. Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic.
    Ali Salari, Yohan Chatelain, Gregory Kiar, Tristan Glatard. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE, MICCAI) (2021) pp. 14–23. Springer Publishing.
  5. Automatic exploration of reduced floating-point representations in iterative methods.
    Yohan Chatelain, Eric Petit, Pablo de Oliveira Castro, Ghislain Lartigue, David Defour. (2019, August). In the European Conference on Parallel Processing (Euro-Par) (pp. 481-494). Springer, Cham.
  6. VeriTracer: Context-enriched tracer for floating-point arithmetic analysis.
    Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, David Defour, Jordan Bieder, and Marc Torrent. In 2018 IEEE 25th Symposium on Computer Arithmetic (ARITH) (pp. 61-68). IEEE

Preprints

  1. A numerical variability approach to results stability tests and its application to neuroimaging
    Yohan Chatelain, Loïc Tetrel, Christopher J Markiewicz, Mathias Goncalves, Gregory Kiar, Oscar Esteban, Pierre Bellec, Tristan Glatard. arXiv:2307.01373
  2. Predicting Parkinson’s disease progression using MRI-based white matter radiomic biomarker and machine learning: a reproducibility and replicability study
    Mohanad Arafe, Nikhil Bhagwat, Yohan Chatelain, Mathieu Dugre, Andrzej Sokolowski, Michelle Wang, Yiming Xiao, Madeleine Sharp, Jean-Baptiste Poline, Tristan Glatard. bioRxiv:2023.05.05.539590.

Communications at international conferences (asbtract)

  1. A numerical variability approach to results stability tests and its application to neuroimaging.
    Yohan Chatelain, Loïc Tetrel, Christopher J. Markiewicz, Mathias Goncalvez, Gregory Kiar, Oscar Esteban, Pierre Bellec and Tristan Glatard. OHBM 2022, Glasgow, Scotland.
  2. Fuzzy environments for the perturbation, evaluation, and application of numerical uncertainty via MCA in the scientific Python ecosystem
    Gregory Kiar, Yohan Chatelain, Ali Salari, Eric Petit, Pablo de Oliveira Castro, and Tristan Glatard. SciPy Conference, 2021.
  3. Towards Abinit on ExaScale supercomputers: the challenge for electronic structure physicists
    Jordan Bieder, Marc Torrent, and Yohan Chatelain. APS Meeting Abstracts. 2018

Communications

  1. IXPUG 2019: Intel Extreme Performance Users Group, CERN, Geneva, Switzerland
  2. IXPUG 2018: Intel Extreme Performance Users Group, Intel Corporation, Hillsboro, OR, USA
  3. ESTN 2018: 8èmes École Thématique de Simulation Numérique, Cargèse, 2018
  4. RAIM 2017: 9èmes Rencontres «Arithmétique de l’Informatique Mathématique», Lyon, 2017
  5. ABIDEV 2017: The 8th ABINIT developer’s workshop, Frejus, 2017

PhD Thesis

  1. Outils de débogage et d’optimisation des calculs flottants dans le contexte HPC
    Yohan Chatelain. Université Paris-Saclay