Founding Researcher, fast.ai
Former President & Chief Scientist, Kaggle
Former Founding CEO, Enlitic
Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai.
Previously, Jeremy was the founding CEO Enlitic, which was the first company to apply deep learning to medicine, and was selected as one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects.
He has many media appearances, including writing for the Guardian, USA Today, and the Washington Post, appearing on ABC (Good Morning America), MSNBC (Joy Reid), CNN, Fox News, BBC, and was a regular guest on Australia’s highest-rated breakfast news program. His talk on TED.com, “The wonderful and terrifying implications of computers that can learn”, has over 2.5 million views. He is a co-founder of the global Masks4All movement.
March 26, 2021
5:30 PM - 6:00 PM (30 minutes) - Coordinated Universal Time
For the first time ever, Jeremy Howard and Rachel Thomas, co-founders of fast.ai, sit down for a fireside chat explaining why they started fast.ai, how it progressed from classes to a software platform, the importance of community, and where they see the future direction of fast.ai. They also dive into the importance of first-principles problem solving and creativity, versus relying on throwing as much compute at the problem as possible.