# Geometry in the Age of Artificial Intelligence and Big Data

## Location

Zoom Lecture

## Event Website

https://www.csbsju.edu/mathematics/pi-conference

## Start Date

17-4-2021 11:00 AM

## End Date

17-4-2021 12:15 PM

## Description

Geometry and Topology are often (mis)taken as pure unapplied parts of Mathematics. With the data science artificial intelligence revolution this false assumption has been shattered once more. In this talk I present two examples of how a geometer can contribute to the growing field of data science, I show how discrete geometry of finite sets of points can be used to understand statistical inference methods such as logistic regression and how basic homology of simplicial complexes plays a role in clustering data and image processing. But perhaps even more surprising, I will show with one example that data science and artificial intelligence may also help mathematical areas such as algebra. The new results I will discuss are joint work I wrote with my Ph.D students Lily Silverstein, Zhenyang Zhang, Tommy Hogan, and Edgar Jaramillo-Rodriguez.

Geometry in the Age of Artificial Intelligence and Big Data

Zoom Lecture

Geometry and Topology are often (mis)taken as pure unapplied parts of Mathematics. With the data science artificial intelligence revolution this false assumption has been shattered once more. In this talk I present two examples of how a geometer can contribute to the growing field of data science, I show how discrete geometry of finite sets of points can be used to understand statistical inference methods such as logistic regression and how basic homology of simplicial complexes plays a role in clustering data and image processing. But perhaps even more surprising, I will show with one example that data science and artificial intelligence may also help mathematical areas such as algebra. The new results I will discuss are joint work I wrote with my Ph.D students Lily Silverstein, Zhenyang Zhang, Tommy Hogan, and Edgar Jaramillo-Rodriguez.

https://digitalcommons.csbsju.edu/math_pi_mu_epsilon/2021/keynote/1