A Mathematical Analysis of Mathematical Faculty

TL;DR

This paper analyzes data from US math faculty to explore relationships among academic variables and proposes that advanced models could automate fairer, more efficient faculty promotion decisions.

Contribution

It applies multiple statistical and machine learning methods to faculty data, demonstrating potential for automated, unbiased promotion algorithms.

Findings

Advanced models can predict faculty promotion outcomes.

Machine learning methods outperform traditional statistical approaches.

Potential for automated, fairer faculty evaluation systems.

Abstract

We use the data of tenured and tenure-track faculty at ten public and private math departments of various tiered rankings in the United States, as a case study to demonstrate the statistical and mathematical relationships among several variables, e.g., the number of publications and citations, the rank of professorship and AMS fellow status. At first we do an exploratory data analysis of the math departments. Then various statistical tools, including regression, artificial neural network, and unsupervised learning, are applied and the results obtained from different methods are compared. We conclude that with more advanced models, it may be possible to design an automatic promotion algorithm that has the potential to be fairer, more efficient and more consistent than human approach.