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Song Han named to MIT Technology Review list of Innovators Under 35

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EECS Assistant Professor Song Han

EECS Staff

EECS faculty member Song Han has been named to MIT Technology Review’s prestigious annual Innovators Under 35 list in the Pioneer category.

Han, who is the Robert J. Shillman (1974) Career Development Assistant Professor in EECS, was recognized for designing software that let powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.

Han’s “deep-compression” technique makes it possible to compress deep neural networks by 10 to 50 times and run — on a smartphone, in real time — “AI algorithms that can recognize objects, generate imagery, and understand human language,” MIT Technology Review noted in its announcement, adding that Facebook and other companies use the technology.

He also cofounded a startup company called DeePhi Tech providing solutions for efficient deep learning computing, which was acquired by Xilinx last year.

Han received a PhD in electrical engineering from Stanford University. He joined the EECS faculty in July 2018, and is a core faculty member in MIT’s Microsystems Technology Laboratories (MTL) and an affiliate member of the Laboratory for Information and Decision Systems (LIDS).

His work has been featured by O’Reilly, IEEE Spectrum, TechEmergence, The Next Platform, and MIT News, among others. He received best-paper awards at the International Conference on Learning Representations and the International Symposium on Field Programmable Gate Arrays. Other honors include a Facebook Research Award, an Amazon Machine Learning Research Award, and a Sony Faculty Award.

Every year, MIT Technology Review’s Innovators Under 35 list recognizes 35 “exceptionally talented technologists whose work has great potential to transform the world.” Categories include Entrepreneurs, Humanitarians, Inventors, Pioneers, and Visionaries.

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Wednesday, June 26, 2019 - 2:45pm

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The EECS faculty member was recognized for designing technology that lets powerful artificial intelligence programs run more efficiently on low-power hardware.

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