SciPy 2023

Stephannie Jimenez Gacha

I've been working in open source since 2019 as part of multiple projects involving scientific computing and IDE development. The last two years a lot of my work has been focused on providing a better UI/UX of multiple applications. I've given multiple talks about different topics, the two most recent are available in the following links:

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Accessibility best practices for authoring Jupyter notebooks
Stephannie Jimenez Gacha, Isabela Presedo-Floyd

So you’ve written the perfect notebook, but do you know who can read it? As a notebook author you have great stories, code, and visualizations filling your work, but how often do you consider accessibility? Jupyter notebooks seem like they are for everyone, but how a notebook gets written can greatly impact how usable it is for people with disabilities. We’ve curated authoring-focused best practices for notebook content to help your notebooks be more inclusive and reach a wider audience.

General Track
Amphitheater 204
Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem
Aaron Meurer, Thomas J. Fan, Stephannie Jimenez Gacha, John Kirkham, Stephan Hoyer, Tyler Reddy, Leo Fang, Matthew Barber, Ralf Gommers, Andreas Mueller, Athan Reines, Mario, Alexandre Passos, Travis E Oliphant, Saul shanabrook

The array API standard ( is a common specification for Python array libraries, such as NumPy, PyTorch, CuPy, Dask, and JAX.

This standard will make it straightforward for array-consuming libraries, like scikit-learn and SciPy, to write code that uniformly supports all of these libraries. This will allow, for instance, running the same code on the CPU and GPU.

This talk will cover the scope of the array API standard, supporting tooling which includes a library-independent test suite and compatibility layer, what work has been completed so far, and the plans going forward.

General Track
Amphitheater 204