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Dec. 16, 2016

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Dec. 16, 2016
For Immediate Release

WATERLOO – Wilfrid Laurier University alumnus Jon Krohn (B.Sc. '07), the chief data scientist at untapt, will speak at Laurier about how individuals can build their own deep learning algorithms. His talk, “Deep Learning with Artificial Neural Networks,” will take place Jan. 4, 2017, at 4 p.m. in Bricker Academic BA101 on Laurier’s Waterloo campus.

Krohn will use biological neurons and the primate visual system as analogies to describe how layers of artificial neurons facilitate deep learning. His talk will empower audience members to build their own deep learning algorithms and will survey contemporary applications for the algorithms.

The subject of deep learning algorithms is particularly relevant given their role in, for example, Tesla's self-driving sedans, Facebook's newsfeed, Apple's voice recognition, and Google inbox's suggested replies. Krohn argues that despite their tremendous real-world utility, these artificial intelligence techniques — developed by machine-learning researchers working at the intersection of statistics and computer science — are not tremendously complex.

Krohn is the chief data scientist at untapt, a machine learning-driven recruitment platform based in New York. He has previously deployed algorithms to automate predictions in the fields of biology, finance, and digital advertising.

Before obtaining his doctorate in neuroscience at Oxford University as a Wellcome Trust Scholar, Krohn studied and conducted research at Laurier. He has published in influential peer-reviewed journals like NIPS, Neurology and Nature Genetics.

Presented by Laurier’s Faculty of Science, “Deep Learning with Artificial Neural Networks” is open to the public and all members of the university community.

For more information, contact Jeanette Haas (519-884-0710 x2427), senior administrative assistant to the dean of the Faculty of Science.

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Kevin Crowley, Director
Communications & Public Affairs
Wilfrid Laurier University

T: 519.884.0710 x3070



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