Bio: Ranthony A. Clark is a National Science Foundation Ascending Postdoctoral Fellow, Phillip Griffiths Assistant Research Professor, and Computational and Mathematical Science Fellow for the Center for Computational Thinking at Duke University. She earned a PhD in Mathematics in 2018 from the University of Iowa and her research interests include applied algebraic topology, data science, commutative ring theory, math education, and the history of Black mathematicians.

Dr. Clark is deeply invested in quantitative justice, that is, using mathematical tools to address societal issues rooted in inequity. Her current work in quantitative justice involves applications of mathematics and data science to electoral redistricting.  She was a Berlekamp Postdoctoral Fellow for the Fall 2023 Semester program on Algorithms, Fairness, and Equity at the Simons Laufer Mathematical Sciences Institute (SLMath) and currently works with the Quantitative Gerrymandering Group in the Department of Mathematics at Duke University. Recently, she became an inaugural member of the Race and Redistricting Expert Project (RREP) 2024 class through the Southern Poverty Law Center.

Additional information can be found here.

Topics include:

Quantitative Justice: Using Math to Change the World

Quantitative Justice comprises the mathematical, computational, and statistical analysis of real world problems related to social inequity. In this context, mathematical tools are used to quantify notions of ‘fairness’ in a given domain, generating both new mathematics and impacting society at large.

In this talk we will introduce this emergent new field of interdisciplinary research. In particular, we will give current examples of how math like statistics, metric geometry, and topological data analysis is being used to shift societal systems, and discuss how this research complements historical and current efforts to broaden participation in the mathematical community through scholarship, teaching, and service.

Death to Determinants

Determinants? Who needs them? “Computing the characteristic polynomial C(x) of a square matrix A as the determinant of the matrix xI-A is like programming in LISP, Lots of Irritating Single Parenthesis.”

The above quote is pulled from the introduction of a paper written by Dr. William McWorter, Jr. the first Black person to earn a PhD in mathematics from Ohio State, which described a determinant-free algorithm for the characteristic polynomial. Dr. McWorter, among many things, aimed to have as one of his mathematical contributions—the ‘dearth’ of determinants in linear algebra.

In this talk, we will discuss McWorter’s algorithm, and the larger project from which it came–Hidden Figures Revealed. Along the way I will discuss how I have used storytelling as a tool in research and teaching to build community in mathematics.

Political Geometry: Metric Based Approaches to Shape Comparison in Redistricting

In this talk we introduce the concept of a metric space, and motivate the use of metrics as a tool for shape comparison. By representing an object as a finite metric space, we can utilize families of metrics like Hausdorff and Gromov-Hausdorff distances to develop similarity measures between shapes.

One particular focus is on applications to electoral redistricting, where the notion of shape is ubiquitous when investigating the political geography of a state. In particular, we will discuss how metrics were used in a recent effort to quantify the idea of ‘communities of interest’ in the 2021 redistricting cycle.