persistent homology

Wednesday, August 15, 2018 - 1:30pm - 2:00pm
Bryn Keller (Intel Corporation)
Discovering a new drug costs more than a billion dollars and takes ten years or
more. So researchers are always looking for new ways to use computers to find
more promising candidate drugs. We present a promising new system that applies
the two-parameter persistent homology to the
complex task of finding good drug candidates, by finding similarities in the 3D
shapes of the molecules.
Monday, August 13, 2018 - 10:30am - 11:30am
Lori Ziegelmeier (Macalester College)
In this talk, we will discuss a variety of applications of persistent homology. We will begin with a quick overview of some of the classic examples of applications such as sensor networks and natural imagery. Then, we will explore a number of additional applications such as brain arteries, hyperspectral imagery, dynamical systems, and biological aggregations. Throughout, we will discuss methods to combine topological features with machine learning.
Tuesday, May 22, 2018 - 1:30pm - 2:30pm
Bei Wang (The University of Utah)
A fundamental question in the study of high-dimensional data is as
follows: Given high-dimensional point cloud samples, how can we infer
the structures of the underlying data?
In manifold learning, we assume the data is supported by a
low-dimensional space with a manifold structure.
However, such an assumption may be too restrictive in practice when we are given point cloud samples not of a manifold but of a stratified space, which contain singularities and mixed dimensionality.
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