Goals

1. Advance Student-Centered, Applied Learning

Provide students from diverse majors with real-world, hands-on research experiences that enhance critical thinking, collaboration, and problem-solving skills. Through long-term, team-based projects, students gain exposure to both advanced technology tools (AI, ML, data analytics) and the complex social realities of health inequity, preparing them for careers in healthcare, data science, social advocacy, and more.

graphic design of health equity workers

2. Foster Multidisciplinary Collaboration

Bring together students and faculty from from various disciplines to work on shared research challenges. By learning to communicate across disciplines, students develop a more holistic understanding of health equity and innovation—an essential skill for addressing complex, real-world problems.

3. Conduct Research with Relevance

Pursue projects that are co-developed with community partners to ensure that all research efforts are practical, ethical, and responsive to real needs. Potential topics include rural mental health, chronic disease prediction, maternal health disparities, and technology access. Research findings will be designed for real-world application and impact.

4. Empower Community Engagement and Impact

Strengthen partnerships between Ñý¼§Ö±²¥and local organizations, public health agencies, and healthcare systems. Community partners play a key role in shaping project design and implementation, ensuring that solutions are inclusive, sustainable, and scalable.

5. Promote Equity-Driven Innovation

Develop and test technologies that explicitly aim to reduce health disparities and ensure equitable access to care. Students will explore the ethical dimensions of innovation and build tools that reflect cultural humility, fairness, and inclusion.

Experience Gained

Working with community stakeholders, students will begin by identifying specific health equity challenges—such as rural mental health, maternal care access, or chronic disease disparities—using real-world data from public health sources, community health centers, and national datasets. They will conduct literature reviews, learn about social determinants of health, and explore how structural inequities impact health outcomes.

Using tools like artificial intelligence (AI), machine learning (ML), and data visualization software, students will clean, analyze, and interpret data to uncover trends and inform solutions. They may develop predictive models, create interactive dashboards, or design technology-based interventions like mobile apps, decision-support tools, or digital health education platforms.

Students will also work closely with community partners to ensure that all solutions are culturally appropriate, ethically sound, and grounded in the lived experiences of the populations served. They will engage in regular team meetings, reflect on the social impact of their work, and contribute to reports, presentations, or publications that communicate their findings.

Throughout the project, students will practice interdisciplinary communication, critical thinking, and ethical decision-making. They’ll also gain exposure to research methods, technology development, grant writing, and community engagement strategies.

Majors and Interests Needed

All majors are welcomed.

Team Advisors

Evelina Sterling, Ph.D.
Professor of Sociology
Email: esterlin@kennesaw.edu

Xinyan Zhang, Ph.D.
Associate Professor of Statistics
Email: xzhang47@kennesaw.edu

Nazmus Sakib, Ph.D.
Assistant Professor of Nuclear Engineering
Email: nsakib1@kennesaw.edu