Dena Asta, assistant professor of statistics at The Ohio State University, will discuss non-Euclidean geometry in real-world settings.

There are many real-world settings in which the data has some natural geometry to it. For instance, radar bearings, medical imaging data, and global weather patterns are all examples of data that are most naturally regarded in points in non-Euclidean spaces of various kinds. For another example, networks observed in the real world, such as social networks, are highly structured forms of data whose structures are intrinsically geometric in nature. And often times the nodes in those networks can be parsimoniously modeled as points in a non-Euclidean latent space; an interplay between the differential geometry of the latent space and the geometry of the networks is a rich source of current research in statistical networks.

Join us on Monday, March 27, from 3:10 to 4 p.m. in Hayes 109 to hear this exciting presentation. We hope to see you there!