Natural Language Processing of Army Insider Threat Hub Data

This paper presents a case prioritization system that utilizes a deep learning classification model trained on expert evaluated insider threat cases to label cases as “negligible”, “low”, “medium”, or “high” threat level. This classification model enables a partnership between machine and human that focuses human effort for the greatest impact.

Insider Threat: Criminal Recidivism

This research delves into the relationship between criminal recidivism and insider threat risk, particularly within the U.S. Army. The methodology employed in this research focused on machine learning techniques but now emphasizes Generalized Linear Models (GLMs), to analyze the data and identify predictors of recidivism, which we then relate to Insider Threat.

Participation of Women in the Workforce: An Indicator of Invisible Barriers

A study attempting to understand the current landscape of women’s participation in the U.S. workforce, in 2022 by examining factors like means of transportation, state, and government assistance.​ Gaining insight into factors that impact women’s workforce participation serves to better guide policy and inform leaders on how to facilitate an inclusive workplace.​