Research

My research explores how Artificial Intelligence (AI) and machine learning (ML) can be applied to healthcare, informatics, and predictive analytics. I focus on developing intelligent, ethical, and explainable AI systems that enhance disease detection, improve diagnosis accuracy, and support better decision-making.

I approach AI research from a practical and interdisciplinary perspective, combining computing, healthcare, and business applications. My goal is to make AI technologies more responsible, interpretable, and inclusive — particularly for underrepresented communities and diverse data environments.

Primary Research Themes

• Applied AI and Predictive Modeling: Using machine learning and deep learning to identify disease risk patterns and patient outcomes.

• Health Informatics and Data Systems: Building and managing intelligent, secure data infrastructures for clinical analytics.

• Federated and Privacy-Preserving Learning: Designing distributed AI frameworks that protect sensitive health data.

• AI Education and Workforce Development – Integrating applied AI into curriculum design and experiential learning programs.