This research project explores the relationship between community social capital and corporate gender diversity, focusing on female executives within S&P 1,500 firms. Previous studies have shown that it is important to have more women in top leadership roles in business organizations today because it provides different perspectives and facilitates various leadership styles, promoting innovation and driving performance. Unfortunately, during the sample period of 7 years between 2010 and 2017, only 8% of top executives in C-suit at S&P 1,500 firms were female. To understand why this is the case and suggest a way to improve this case of female underrepresentation in executive positions, I study the community social capital that fosters a culture of trust, cooperation, and mutual respect, which are foundational for creating inclusive work environments that value diversity. I hypothesize that substantial regional social capital promotes a higher representation of women in top management roles. I collected county-level social capital data from Penn State’s Northeast Regional Center of Rural Development (NRCRD) and gender diversity data from the WRDS ExecuComp database. To test my hypothesis, I conducted a regression analysis. Empirical results indicate that sample firms with greater exposure to higher community social capital are more likely to appoint women to top management positions, which allows them to make crucial decisions within the organization. This study highlights the impact of community dynamics on corporate policies and displays a new perspective on promoting gender diversity in leadership positions. This project intends to improve an individual learning experience by providing valuable insights into the interconnection between societal structures and corporate governance, concentrating on gender inclusivity.

Continuous gravitational waves (CW) from sources such as a non-axisymmetric spinning neutron star have not yet been detected. If CW from neutron stars exist, the weak signals could be hidden within noise. The search for CW signals is impeded by the presence of narrow spectral artifacts (lines) caused by instrumentation or the environment at the Laser Interferometer Gravitational Wave Observatory (LIGO). Better identification of line noise would make a detection more likely, and a detection has the potential to expand our current knowledge of neutron stars. A non-machine learning (non-ML) line finding algorithm is currently used on a daily basis at the LIGO Hanford and Livingston Observatories. A different approach to line finding utilizes a machine learning (ML) algorithm. My research uses both the ML and non-ML line finding methods, comparing the accuracy and efficiency of these two methods applied to various data sets. Expected results are that the non-ML method is currently more accurate and efficient from a computing resources perspective, but that the ML approach has the potential for high accuracy and adaptability, and will eventually be more efficient in human hours. The impact of my research is to implement more accurate and efficient line finding algorithms. This work is important because automated line finders at LIGO Hanford and LIGO Livingston save researchers hundreds of hours of work and could be set up to alert researchers on changes in noise behaviors.

This research investigates the impact of ethnic diversity on corporate boards in relation to the frequency of security class actions faced by firms. Security class action lawsuits hold significant concern for today's corporations and society at large due to their implications for accountability, investor protection, and the reinforcement of ethical standards. In this study, our team focuses on ethnic diversity as a vital contributor to enhanced monitoring and ethical practices by integrating varied cultural insights and ethical standards, thereby strengthening corporate governance and ethical oversight. We hypothesize that firms with greater ethnic diversity among their board members are less likely to encounter security class action lawsuits. To test this hypothesis, we sourced data on security class action lawsuits from the Stanford Securities Class Action Clearinghouse, and derived information regarding the ethnicity of board members from the Wharton Research Data Services Institutional Shareholders Services (WRDS ISS) database supplemented by various web sources. Employing a linear regression model to analyze the data, the empirical results substantiate the hypothesis, revealing that S&P1,500 firms with at least one director of minority ethnicity during the sample period of 2009 to 2015 are significantly less likely to face security class action lawsuits. This finding suggests that ethnic diversity on corporate boards is not only a marker of inclusivity but also serves as a protective measure against legal challenges. The study's implication is clear: promoting ethnic diversity within corporate boards is a strategic move for firms and shareholders, potentially reducing litigation risks and associated costs. This research contributes to the broader discourse on corporate governance, emphasizing the practical benefits of ethnic diversity in high-level decision-making bodies.