The HADES seminar on Tuesday, December 5th, will be at 3:30pm in Room 748 (not in 740 this week!).
Speaker: Akshat Kumar
Abstract: Graph Laplacians and Markov processes are intimately connected and ubiquitous in the study of graph structures. They have led to significant advances in a class of geometric inverse problems known as “manifold learning”, wherein one wishes to learn the geometry of a Riemannian submanifold from finite Euclidean point samples. The data gives rise to the geometry-encoding neighbourhood graphs. Present-day techniques are dominated primarily by the low spectral resolution of the graph Laplacians, while finer aspects of the underlying geometry, such as the geodesic flow, are observed only in the high spectral regime.
We establish a data-driven uncertainty principle that dictates the scaling of the wavelength