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Thursday, May 30 • 3:15pm - 3:45pm
Obscuring sensitive information with generative adversarial networks

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Much of the data collected by corporations and public institutions is too sensitive to share publicly, or even with third parties. Medical records in particular are very difficult to release to outside researchers. Generative Adversarial Networks (GANs) may provide a solution to this problem: a GAN can be trained to generate new data that is representative of real data, but without confidentiality issues. In this talk, I will provide a general introduction to GANs, and present results showing that successful models can be trained using only generated data.

avatar for Nicole Bridgland, PhD, MS

Nicole Bridgland, PhD, MS

Data Scientist, World Wide Technology
Nicole Bridgland is a data scientist with World Wide Technology.  She completed her PhD in mathematics at the University of Minnesota in 2018. 

Thursday May 30, 2019 3:15pm - 3:45pm CDT
(E) P0806 A&B Normandale Partnership Center, 9700 France Ave So, Bloomington, MN 55431