The signal demixing problem seeks to separate the superposition of multiple signals into its constituent components. I will describe a geometric view of the superposition process, and how the duality of convex cones allows us to develop an efficient algorithm for recovering the components with sublinear iteration complexity and linear storage. If the signal measurements are random, this process stably recovers low-complexity and incoherent signals with high probability and with optimal sample complexity. This is joint work with my students and postdocs Zhenan Fan, Halyun Jeong, and Babhru Joshi.

RSVP here.