## Topological Data Analysis—Catherine Pfaff

### Monday, October 7th, 2019

**Time:** 2:30 p.m. **Place:** Goodes Hall 120

**Speaker:** Catherine Pfaff

**Topic:** Mapper Algorithm

Previous attendance or knowledge will not be required. All are welcome!

University Wide

Faculty/School Portals

Queen's University
## Search/Login Toggle

Topological data analysis (TDA) is a class of topological techniques for understanding large, noisy, possibly incomplete data. The techniques have a particular focus on understanding the large-scale coordinate-independent "shape" of the data. While perhaps the most famous application of TDA is the discovery of a new type of breast cancer from an old dataset, the techniques are finding use in everything from medical imaging to progression analysis of disease, signal analysis, viral evolution, complex networks, AI, & much more (wikipedia has a more extensive list of applications). The aim of this seminar is to build a common language for understanding TDA & then hopefully to shift to a focus on applications. The tentative structure involves learning and sharing knowledge on dimensionality reduction, clustering, persistent homology, probabilistic analysis and homology inference in a format that includes different seminar members sharing knowledge through prepared presentations, coupled with group discussion.

**Time:** 2:30 p.m. **Place:** Goodes Hall 120

**Speaker:** Catherine Pfaff

**Topic:** Mapper Algorithm

Previous attendance or knowledge will not be required. All are welcome!

**Time:** 2:30 p.m **Place:** Goodes Hall 120

**Speaker:** Luke Steverango, Troy Zeier

**Topic:** Simplicial complexes approximating data

**Time:** 2:30 p.m **Place:** Goodes Hall 120

**Speaker:** Mikhail Nediak (Smith School of Business)

**Topic:** Clustering

**Time:** 2:30 p.m **Place:** Goodes Hall 120

**Speaker:** Mikhail Nediak & Catherine Pfaff

**Topic:** Introductory Meeting