Calendar

Data in high dimensional Euclidean space may often be described with fewer parameters than the ambient space, perhaps lying on or near a submanifold of lower intrinsic dimension. In part one of this talk, we will discuss a new method for estimating this intrinsic dimension from the data: a version of local PCA which is calibrated on quadratic embeddings which may better approximate a manifold at larger scales. The second part of the talk will focus on a technique of random embeddings to reduce ambient dimension. The topics of this talk are joint work with Anna Gilbert.

Titles of the mini-talks:

Or Landesberg: (Non-)Rigidity of horocycle dynamics in geometrically infinite spaces

Kevin O’Neill: Nonlinear variants of the Whitney Extension Problem

Ka Ho Wong: Volume conjectures for quantum invariants

Abinand Gopal: Using rational functions to solve PDEs