Honorary Professor
Overview
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, and is an honorary professor at the University of Queensland. Previously, Jeremy was a Distinguished Research Scientist at the University of San Francisco, where he was the founding chair of the Wicklow Artifical Intelligence in Medical Research Initiative.
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 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.
Publications
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Journal Article: Deep learning-based point-scanning super-resolution imaging
Fang, Linjing, Monroe, Fred, Novak, Sammy Weiser, Kirk, Lyndsey, Schiavon, Cara R., Yu, Seungyoon B., Zhang, Tong, Wu, Melissa, Kastner, Kyle, Latif, Alaa Abdel, Lin, Zijun, Shaw, Andrew, Kubota, Yoshiyuki, Mendenhall, John, Zhang, Zhao, Pekkurnaz, Gulcin, Harris, Kristen, Howard, Jeremy and Manor, Uri (2021). Deep learning-based point-scanning super-resolution imaging. Nature Methods, 18 (4), 406-416. doi: 10.1038/s41592-021-01080-z
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Journal Article: An evidence review of face masks against COVID-19
Howard, Jeremy, Huang, Austin, Li, Zhiyuan, Tufekci, Zeynep, Zdimal, Vladimir, van der Westhuizen, Helene-Mari, von Delft, Arne, Price, Amy, Fridman, Lex, Tang, Lei-Han, Tang, Viola, Watson, Gregory L., Bax, Christina E., Shaikh, Reshama, Questier, Frederik, Hernandez, Danny, Chu, Larry F., Ramirez, Christina M. and Rimoin, Anne W. (2021). An evidence review of face masks against COVID-19. Proceedings of the National Academy of Sciences of the United States of America, 118 (4) e2014564118. doi: 10.1073/pnas.2014564118
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Journal Article: Fastai: a layered API for deep learning
Howard, Jeremy and Gugger, Sylvain (2020). Fastai: a layered API for deep learning. Information , 11 (2) 108, 108. doi: 10.3390/info11020108
Publications
Journal Article
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Deep learning-based point-scanning super-resolution imaging
Fang, Linjing, Monroe, Fred, Novak, Sammy Weiser, Kirk, Lyndsey, Schiavon, Cara R., Yu, Seungyoon B., Zhang, Tong, Wu, Melissa, Kastner, Kyle, Latif, Alaa Abdel, Lin, Zijun, Shaw, Andrew, Kubota, Yoshiyuki, Mendenhall, John, Zhang, Zhao, Pekkurnaz, Gulcin, Harris, Kristen, Howard, Jeremy and Manor, Uri (2021). Deep learning-based point-scanning super-resolution imaging. Nature Methods, 18 (4), 406-416. doi: 10.1038/s41592-021-01080-z
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An evidence review of face masks against COVID-19
Howard, Jeremy, Huang, Austin, Li, Zhiyuan, Tufekci, Zeynep, Zdimal, Vladimir, van der Westhuizen, Helene-Mari, von Delft, Arne, Price, Amy, Fridman, Lex, Tang, Lei-Han, Tang, Viola, Watson, Gregory L., Bax, Christina E., Shaikh, Reshama, Questier, Frederik, Hernandez, Danny, Chu, Larry F., Ramirez, Christina M. and Rimoin, Anne W. (2021). An evidence review of face masks against COVID-19. Proceedings of the National Academy of Sciences of the United States of America, 118 (4) e2014564118. doi: 10.1073/pnas.2014564118
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Fastai: a layered API for deep learning
Howard, Jeremy and Gugger, Sylvain (2020). Fastai: a layered API for deep learning. Information , 11 (2) 108, 108. doi: 10.3390/info11020108
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Open letter on the digital economy
Brynjolfsson, Erik, McAfee, Andy, Jurvetson, Steve, O'Reilly, Tim, Manyika, James, Tyson, Laura, Benioff, Marc, Bass, Carl, Schoendorf, Joe, Bresnahan, Tim, Khosla, Vinod, Howard, Jeremy, Spence, Michael, Suleyman, Mustafa, Stern, Scott and Kirkpatrick, David (2015). Open letter on the digital economy. Technology Review, 118 (4), 11-12.
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De-identification methods for open health data: The case of the heritage health prize claims dataset
Emam, Khaled, Arbuckle, Luk, Koru, Gunes, Eze, Benjamin, Gaudette, Lisa, Neri, Emilio, Rose, Sean, Howard, Jeremy and Gluck, Jonathan (2012). De-identification methods for open health data: The case of the heritage health prize claims dataset. Journal of Medical Internet Research, 14 (1) e33. doi: 10.2196/jmir.2001
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Winning methods for forecasting tourism time series
Baker, Lee C. and Howard, Jeremy (2011). Winning methods for forecasting tourism time series. International Journal of Forecasting, 27 (3), 850-852. doi: 10.1016/j.ijforecast.2011.03.003
Conference Publication
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Applying a pre-trained language model to Spanish twitter humor prediction
Farzin, Bobak, Czapla, Piotr and Howard, Jeremy (2019). Applying a pre-trained language model to Spanish twitter humor prediction. CEUR-WS.
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Universal language model fine-tuning for text classification
Howard, Jeremy and Ruder, Sebastian (2018). Universal language model fine-tuning for text classification. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 15-20 July 2018. Stroudsburg, PA, United States: Association for Computational Linguistics . doi: 10.18653/v1/p18-1031
