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I am a postdoctoral researcher in the Big Data Infrastructures for Neuroinformatics lab at Concordia University, Montreal, Canada. My research topics include computer arithmetic, high-performance computing, and reproducibility. My work aims to democratize the use of stability analysis of scientific computing codes through automatic tools to improve numerical quality.
Posts
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Floacon: A Web-Based Floating-Point Converter and Explorer
Floacon, a new web-based tool designed to help understand and explore floating-point numbers. This project aims to provide an interactive way to visualize floating-point formats, and to experiment with custom formats.
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New paper accepted (GigaScience 2025)
I am thrilled to announce that the paper titled “An analysis of performance bottlenecks in MRI preprocessing“ has been accepted at GigaScience 25.
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New paper (IEEE TC 2024)
I am thrilled to announce that the paper titled “A numerical variability approach to results stability tests and its application to neuroimaging” has been accepted for publication in IEEE Transactions on Computers (TC) journal.
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Best Paper Award (ACM REP 24)
I am thrilled to announce that the paper titled “The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging“ has been awarded the Best Paper Award at ACM REP 24.
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Significant digits viewer
This post demonstrates how to visualize numerical instability in floating-point computations by simulating instability in Chebyshev polynomials using Monte Carlo Arithmetic with Verificarlo and displaying the results as an animated GIF.