Fall 2024
LaDrea Ingram, Senior Health Services Researcher, Atrium Health
Project Title: Integrative Strategies for Community Resilience and Public Health Advancement: A Collaborative Fellowship
Academic Unit/Department: Yale School of Public Health
Institutional Partner: Prairie View A&M University’s School of Public and Allied Health
Project Description: Dr. LaDrea Ingram’s fellowship focuses on enhancing community resilience and public health through the implementation of the Gatherings Network framework. This framework integrates equity-based principles to promote sustainability, growth, and innovation by engaging diverse stakeholders, including faith-based organizations, community partners, students, faculty, and administration. The project is structured into four phases: The first phase involves a literature review and stakeholder consultations to develop a framework that aligns with the community’s needs. The second phase utilizes qualitative case studies and participatory research, including on-site immersion at Prairie View A&M, to gather insights on effective partnership practices. The third phase focuses on data analysis to create a strategic model that integrates community resilience with public health objectives, emphasizing sustainability and innovation. Finally, the fourth phase will apply research findings in real-world settings and disseminate results through academic publications and community workshops, concluding with an impact report and strategic roadmap for the university’s growth from 2025 to 2035.
Spring 2025
Monica Styles, Assistant Professor of Spanish, Howard University
Project Title: Afro-Peruvians in the Colonial Latin American Library Canon
Academic Unit/Department: Faculty of Arts and Sciences – Whitney Humanities Center
Institutional Partner: Howard University
Project Description: Dr. Monica Styles will advance research for two chapters of a book manuscript focused on the Afro-Peruvian presence in 17th-century colonial cultural production. Specifically, Dr. Styles will examine the intersections of race and gender in the writings about and by Afro-Peruvian healers. This research seeks to build on recent scholarship by amplifying historically underrepresented voices in colonial Latin American literature. Key source materials for the project include testimonies from Inquisitorial trials against Afro-Peruvian women accused of witchcraft and healing, as well as autobiographical and biographical writings about prominent Afro-Peruvian healers. The research will explore several central questions: (1) How are ideologies shaped in narratives about Afro-Peruvian healers and spiritual leaders? (2) In what ways do race and gender intersect in these representations? (3) How do Afro-Peruvians engage with and challenge colonial racial narratives, and how have they contested stereotypes about Afro-descendants?
Timothy Oladunni, Assistant Professor, Computer Science, Morgan State University
Project Title: CardiaNet: Neural Networks in Cardiac ECG Signals
Academic Unit/Department: Faculty of Arts and Sciences – Department of Computer Science
Institutional Partner: Morgan State University
Project Description: Dr. Timothy Oladunni’s research project aims to address the global challenge of cardiovascular diseases (CVDs), which are a leading cause of death worldwide. With over 17.9 million deaths annually, early and accurate detection of CVDs is critical. The project focuses on using electrocardiogram (ECG) signals to predict cardiac diseases, analyzing key morphological features such as the PQRST complex and the QRS complex. By detecting key fiducial points in ECG signals, the project aims to enhance early disease detection through neural network classifiers. Building on previous work, including a COVID-19 classifier using both deep neural networks and machine learning algorithms, the project seeks to fill a gap in the literature by creating a robust neural network model to predict cardiac diseases in the context of evolving diseases like COVID-19. The study will incorporate data exploration, deep neural network modeling, and the identification of key parameters in ECG signals.