Ashley Anderson
I am one of the less-active napari core devs, with most of my contributions these days coming through vispy or on the web infrastructure (npe2api and napari hub). I first started working on napari as part of the CZI Imaging Tech team, but I now participate primarily in my spare time.
I got into Python and open source while studying Medical Physics at UW-Madison. After a few years in academia at Barrow Neurological Institute (Phoenix, AZ), I made the switch to industry. I first worked as an on-site clinical MRI scientist for Philips (Mayo Clinic, Rochester, MN), then joined a low-field MRI startup called Hyperfine (Guilford, CT). I'm now a remote worker for Biohub and still reside in Guilford, CT; but I'm in the middle of a move to Dobbs Ferry, NY.
Session
With everything from microscopes to telescopes to satellites, scientists produce image data in countless formats, shapes, sizes, and dimensions. Python provides a rich ecosystem of libraries to make sense of them. napari is a Python library for multidimensional image visualization, but it does double duty as a standalone application that can be easily extended with GUI tools for analysis, visualization, and annotation. In this tutorial, we'll start with the basics of image visualization and analysis in napari, then show how to extend the napari user interface to make analysis workflows as easy as pushing a button, and finally show how to share these extensions as plugins, which can be easily installed by users and collaborators. If you work with images (particularly multidimensional images), and especially if you work with scientists who may not be comfortable with Python, this tutorial might be for you!
Installation Instructions: https://napari.org/workshops/extend/setup/