But her passion is for paper--with no scissors. Today, she's a tessellation expert who teaches, invents new designs, and writes papers on the underlying math. Madonna Yoder '17 photographed in her studio Ross Mantle When Madonna Yoder '17 was eight years old, she learned how to fold a square piece of paper over and over and over again. After about 16 folds, she held a bird in her hands. The first time she pulled the tail of a flapping crane, she says, she realized: . That first piece was an origami classic, folded by kids at summer camp for generations and many people's first foray into the art form.

Last week GE Healthcare launched the Edison Developer Program to encourage innovation and better deploy artificial intelligence applications for providers. The program will give smaller companies more opportunities to integrate their AI into GE products. Ultimately, GE hopes to make its offerings more efficient and user-friendly. "As we looked across our portfolio of diagnostics, therapeutics and monitoring, we really felt compelled that the only way we could derive the outcome changes needed for our customers, providers and patients, at a global scale, was to drive a digital thread through the GE Healthcare product line that leverages artificial intelligence among many other digital capabilities," said Karley Yoder, GE Healthcare's VP and GM of AI, in a phone interview. The developer program builds on the existing Edison platform, which leverages partnerships with corporate and academic researchers to funnel AI into GE products.

We model messaging activities as a hierarchical doubly stochastic point process with three main levels, and develop an iterative algorithm for inferring actors' relative latent positions from a stream of messaging activity data. Each of the message-exchanging actors is modeled as a process in a latent space. The actors' latent positions are assumed to be influenced by the distribution of a much larger population over the latent space. Each actor's movement in the latent space is modeled as being governed by two parameters that we call confidence and visibility, in addition to dependence on the population distribution. The messaging frequency between a pair of actors is assumed to be inversely proportional to the distance between their latent positions. Our inference algorithm is based on a projection approach to an online filtering problem. The algorithm associates each actor with a probability density-valued process, and each probability density is assumed to be a mixture of basis functions. For efficient numerical experiments, we further develop our algorithm for the case where the basis functions are obtained by translating and scaling a standard Gaussian density.