Caltech/Lawrence Livermore National Lab
Most of us are here because we’re already using and excited about Julia. How can we make the language and ecosystem more inclusive of and accessible to potential new users coming from all backgrounds and experience levels? I believe the keys are creating fantastic teaching materials and making these materials free, open, and visible. We want to make sure that the barrier to learning Julia is low — and not just for experienced programmers. I will share with you efforts underway in the Julia community to develop and distribute accessible materials to learn Julia, including in-person outreach at schools around North America and an online tutorial series. Of course, our focus cannot simply be on teaching Julia, but on using Julia to teach other in-demand skills, like programming and data science. To this end, I will also discuss the development of machine learning curriculum using Julia and ways that you can get involved in making the Julia language and ecosystem more accessible.
Jane is a graduate student in computational materials physics enrolled at Caltech. She is interning at Lawrence Livermore National Lab, where she is working with Xavier Andrade on methods for and applications of density functional theory.
