Abstract: Most of us have been there: your code works, but it needs to run faster. You start thinking about making it run in parallel - now how to go about that? Or you discover it already does some things, but not everything, in parallel - how do you build on that without running into unexpected problems?
Bio: Ralf has been deeply involved in the SciPy and PyData communities for over a decade. He is a maintainer of NumPy, SciPy and data-apis.org, and has contributed widely throughout the SciPy ecosystem. Ralf is currently the SciPy Steering Council Chair, and he served on the NumFOCUS Board of Directors from 2012-2018. Ralf co-directs Quansight Labs, which consists of developers, community managers, designers, and documentation writers who build open-source technology and grow open-source communities around data science and scientific computing projects. Previously Ralf has worked in industrial R&D, on topics as diverse as MRI, lithography and forestry.