Jeremy Howard


Networks & Computing Systems, Data Science

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Areas of Expertise

Networks & Computing Systems  •  Medicine  •  Data Science  •  Artificial Intelligence  •  Deep Learning

About Jeremy

Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at, a research institute dedicated to making deep learning more accessible. He is also a faculty member at the University of San Francisco, and is Chief Scientist at and

Previously, Jeremy was the founding CEO of 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 many others. His talk on, “The wonderful and terrifying implications of computers that can learn”, has over 2.5 million views. He is the co-founder of the international #Masks4All movement.

Big Data & Machine Learning | Jeremy Howard | Exponential Medicine 2015

Future of Data Science with Jeremy Howard

The wonderful and terrifying implications of computers that can learn | Jeremy Howard | TEDx

TEDxBrussels - Nov 2018

Lesson 4: Deep Learning 2019 - NLP; Tabular data; Collaborative filtering; Embeddings

Future of Individualized Medicine 2019

Speaking Topics

  • Deep Learning in Practice

    Recent advances in AI have made it possible for computers to do things they couldn’t do before, such as see, hear, and read. In this talk we’ll discover what this means in practice.

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