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Women of color (WOC) professors confront distinct problems in academic institutions due to the intersection of their gender, cultural, and racial identities. Using role congruity theory and intersectionality as a conceptual framework, this study investigated how bias influenced the experiences of WOC professors in the STEM disciplines and the consequences for advancement. Role congruity theory evaluates leadership conduct less positively when performed by a woman, which makes it harder for women to achieve or persist in leadership roles (Eagly & Karau, 2002). As such, we examined the intricate interaction of role congruity and intersectionality (compounded oppression due to having multiple marginalized identities) among WOC professors through in-depth interviews with three tenured and five pre-tenured WOC faculty members from various US universities. The findings offer insight into their' challenges with unwelcoming instructional environments, institutional culture, and prejudice towards their leadership roles, undermining their evaluation and professional development. Furthermore, this study emphasizes the critical need for institutional reform in creating supportive and inclusive workplaces for WOC scholars. 

Interactive web-based applications play an important role for both service providers and consumers. However, web applications tend to be complex, produce high-volume data, and are often ripe for attack. Attack analysis and remediation are complicated by adversary obfuscation and the difficulty in assembling and analyzing logs. In this work, we explore the web application analysis task through log file fusion, distillation, and visualization. Our approach consists of visualizing the logs of web and database traffic with detailed function execution traces. We establish causal links between events and their associated behaviors. We evaluate the effectiveness of this process using data volume reduction statistics, user interaction models, and usage scenarios. Across a set of scenarios, we find that our techniques can filter at least 97.5% of log data and reduce analysis time by 93-96%.