Title: Ingredients matter: Quick and easy recipes for estimating clusters, manifolds, and epidemics
Speaker: Dustin Mixon
Abstract: Data science resembles the culinary arts in the sense that better ingredients allow for better results. We consider three instances of this phenomenon. First, we estimate clusters in graphs, and we find that more signal allows for faster estimation. Here, "signal" refers to having more edges within planted communities than across communities. Next, in the context of manifolds, we find that an informative prior allows for estimates of lower error. In particular, we apply the prior that the unknown manifold enjoys a large, unknown symmetry group. Finally, we consider the problem of estimating parameters in epidemiological models, where we find that a certain diversity of data allows one to design estimation algorithms with provable guarantees. In this case, data diversity refers to certain combinatorial features of the social network. Joint work with Jameson Cahill, Charles Clum, Hans Parshall, and Kaiying Xie.
Note: This is part of the Invitations to Mathematics lecture series given each year in Autumn Semester. Pre-candidacy PhD students can sign up for this lecture series by registering for two credit hours of Math 6193 with Professor Nimish Shah.
