Date of Award

2022

Document Type

Dissertation/Thesis

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Zhu, Xingquan

Abstract

In order for innovation and breakthroughs to occur, principal investigators must constantly apply for grants and other funding sources. Through previous research, it has been shown that peer-review panels responsible for selecting grant award recipients don’t base their decisions on the applicant’s academic or research history and affiliations. Instead, they can identify quality research proposals that achieve high citation counts later on. Therefore, it can be deduced that the recipients are chosen solely due to their research quality and topic with little to no bias involved. This produces two important questions: Can machine learning help predict the success of faculty seeking external awards? What are the important factors related to such predictive models? Using the Academic Analytics Research Center’s rich faculty dataset, I will leverage machine learning models to identify important factors associated with winning grant awards.

Comments

Thesis (B.S.)--Florida Atlantic University, Harriet L. Wilkes Honors College, 2022

Extent

42 p.

Identifier

FAUHT00192

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