JupyterCon 2020 keynote speaker announcement
Jeremy Howard is co-founder of fast.ai, and researcher in residence on medical data science at the University of San Francisco. He is Chief Scientist at platform.ai, and held this role previously at doc.ai and Kaggle, where he was also President. Jeremy is a serial entrepreneur, having founded several successful companies after starting his career in management consulting. His most recent, Enlitic, was the first medical deep learning company, which just one year after its founding in 2014 had raised $15 million in two rounds of funding. He left the company two years after.
An open educator, Jeremy co-authored free courses on deep learning that have reached hundreds of thousands of learners around the world. He also co-authors an open-source library for deep learning called fastai, first released in 2018. The library sits atop PyTorch to provide a consistent interface for deep learning applications to images, text, time series, data frames and more. The second version of the library was announced in February this year with an arXiv preprint. All this work has recently come together as a book, written openly on Jupyter notebooks. Over the span of just a few years, he and fast.ai co-founder Rachel Thomas have done more to expand the reach and understanding of deep-learning technology than many global technology corporations.
Last March, as the COVID-19 pandemic was spreading around the world, Jeremy co-founded the #Masks4All campaign. It challenged the global consensus advice at the time on wearing face coverings to protect each other from infection. The campaign is estimated to have reached more than a billion people on social media, and was covered by global news outlets. “This has completely taken over my life,”said Jeremy in an April 1st interview. His June-19 epic tweet-thread reviewing the scientific evidence in support of mask-wearing is a paragon in science communication.
Jeremy has contributed to open source software throughout his career. He both contributed to and helped steer the Perl programming language through authoring requests-for-comments and chairing the Perl6-data working group. Among his latest projects is nbdev, a tool for developing Python libraries using Jupyter as the writing environment for both code and documentation. It empowers users to create hyperlinked documentation, Python modules, tests, and pip-installers, all from Jupyter notebooks, realizing the ideal of Knuth's literate programming. And expanding the potential of Jupyter as a writing environment for blogging, fastpages facilitates creating Jekyll blog posts on GitHub pages from content source in Jupyter notebooks. The posts can include interactive data visualizations, embed YouTube videos or Twitter cards, and show code snippets in collapsible blocks. Both these projects delight Jupyter enthusiasts with heaps of creative possibilities!
Even if a journalist writing about the impact of #Masks4all dubbed Jeremy a “little-known data scientist,”counting by GitHub stars and Twitter followers, he is really a data-science luminary. You all know him by now, and will be eager to hear him speak to our community in his JupyterCon keynote.
Like our first announced keynote speaker, Anima Anandkumar, Jeremy shines the light of his creative expression in diverse spheres of life and profession. We are thrilled and grateful to them both for allowing JupyterCon to host them!
— Lorena A Barba, JupyterCon 2020 General Chair
Call for tutorials, talks, and posters extended until July 22nd!
Jeremy Howard was originally published in Jupyter Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.