Abstract: 

Scikit-learn was born as a tool from machine-learning geeks for computer geeks. But it has grown as an industry-standard, used by many with various impacts on the world. Growing up brings new challenges. How can an open-source, community-driven project address the needs of a diverse and huge user base? How to grow from a pure technical focus to facing new responsibilities created by the success of our AI tools? How to prevent burn out from a small set of core contributors and on-board new contributors?

Bio: 

Gaël Varoquaux is an Inria faculty researcher working on data science and brain imaging. He has a joint position at Inria (French Computer Science National research) and in the Neurospin brain research institute. His research focuses on using data and machine learning for scientific inference, applying it to brain-imaging data to understand cognition, as well as developing tools that make it easier for non-specialists to use machine learning. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.