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Compute! Paris @computeparis.bsky.social · 12h

For years, Google Earth Engine has been the only real option for scientists who want to run algorithms across petabytes of satellite data and see results fast. That's starting to change.

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Compute! Paris @computeparis.bsky.social · 29/09/2026

Welcome to our new sponsor: Inria www.inria.fr/en

inria.fr

Accueil | Inria

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Compute! Paris @computeparis.bsky.social · 28/09/2026

Statistical distributions directly in Polars. No .to_numpy(), no round-trip through scipy.stats, no broken laziness or GIL lock. Just Normal(mu=..., sigma=...).cdf(x), one distribution per row, fully vectorized, lazy all the way through.

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Compute! Paris @computeparis.bsky.social · 25/09/2026

Slow down! Drive-by-plotting is not allowed at Compute! Paris this November: @amy8492.bsky.social takes you through deliberately visualizing Open Food Facts, a real, messy, multi-gigabyte open dataset.

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Compute! Paris @computeparis.bsky.social · 23/09/2026

Stop Bruteforcing your Posteriors! Exploit known analytical structure! That's the title and pitch of Tim Weiland's talk at Compute! Paris this November.

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Compute! Paris @computeparis.bsky.social · 21/09/2026

Same seed, same code, same dependencies, even containerization, and still a different result. That's what Christof Seiler hit when running approximate PCA on single-cell genomics data: not cosmetics but numbers that shouldn't move, population counts shifting, rare cell types vanishing.

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Compute! Paris @computeparis.bsky.social · 18/09/2026

Mature bindings with PyO3 and maturin, a zero-copy data interface via Arrow, and LLMs helping to write code. Never was it easier to (re)write Python library internals in Rust.

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Compute! Paris @computeparis.bsky.social · 16/09/2026

TabICL, a Tabular Foundation Model, pretrained once on millions of synthetic data-generating processes, automatically recognizes what shaped your data, and predicts accordingly. Out-of-the-box, well-calibrated predictions, classification & regression, that's the promise.

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Compute! Paris @computeparis.bsky.social · 15/09/2026

Everything in JupyterLab is a plugin, every action is a command, and kernels stand ready to run arbitrary code on demand. What’s great for humans turns out to be amazing for AI agents.

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Compute! Paris @computeparis.bsky.social · 14/09/2026

Meet "future", one of the top 0.6% of R packages and the go-to solution for scaling R with parallel tasks, from laptops to HPC. At Compute! Paris, Henrik Bengtsson (its creator and maintainer since 2015) will show how to simplify its API using R’s metaprogramming and transpilation.

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Compute! Paris @computeparis.bsky.social · 09/09/2026

AI was supposed to do the laundry so we could do the art. In data science, that art is modeling, exploration and understanding the problem. Instead, too often agents get the fun, and we're stuck with the chores: reviewing code, firefighting it in prod.

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Compute! Paris @computeparis.bsky.social · 07/09/2026

Autograding notebooks usually means a server running student code, plus a database tracking submissions and scores. But what if you could skip both?

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Compute! Paris @computeparis.bsky.social · 04/09/2026

Agents judging other agents are black boxes whitewashing black boxes. If you don't find this funny, come to Compute! and hear Gregoire Martinon present a more principled approach: injecting human understanding in a controlled way into the evaluation but still use agents to scale it up.

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Compute! Paris @computeparis.bsky.social · 02/09/2026

GeoPandas is core to working with 2D spatial dimensions in Python: satellite imagery, biological cells, urban planning, anywhere where there's "where".

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Compute! Paris @computeparis.bsky.social · 31/08/2026

Ever put JSON into a Parquet string column and thought there had to be a better way? Rok Mihevc brings good news: the Parquet community has ratified a new column type built specifically for messy, nested data, so columnar storage can finally handle it properly instead of using blobs.

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Compute! Paris @computeparis.bsky.social · 28/08/2026

Big infrastructure again: SciPy is the layer that turns arrays into science with optimization, linear algebra, solvers, statistics, and signal processing.

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Compute! Paris @computeparis.bsky.social · 26/08/2026

2 billion downloads a year. 63 times a second. Scikit-learn is infrastructure, and carrying more weight than ever since AI agents lean on it too.

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Compute! Paris @computeparis.bsky.social · 25/08/2026

Concepts over languages - another value we promote. As AI increasingly helps read, write and translate code, language barriers matter less. Logic, abstractions, trade-offs and real-life experience travel across stacks.

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Compute! Paris @computeparis.bsky.social · 19/08/2026

Makie, Julia's high-performance visualization system, is used from NASA JPL to university lecture halls. Simon Danisch, who created it, walks through where it's heading.

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Compute! Paris @computeparis.bsky.social · 17/08/2026

Nim is a new statically typed, compiled, general purpose programming language for scientific computing. This talk by Pietro Peterlongo, for beginners and programming enthusiasts alike, takes us on a journey through the exotic lands of the language design and Nim's features.

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Compute! Paris @computeparis.bsky.social · 14/08/2026

Organizations are sitting on vast stores of unstructured text, images, and video that vector search can put to use if it can keep up with the scale. In this talk, Mickael Ide examines different techniques as datasets grow into the billions of records.

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Compute! Paris @computeparis.bsky.social · 13/08/2026

🚀 Join the Mission: Volunteer or Apply for a Scholarship at Compute! Paris 2026! 🚀 We’re thrilled to announce that applications are now open for volunteers and scholarships for Compute! Paris 2026! Whether you want to contribute behind the scenes or need support to attend, this is your chance !