Research & Publications
Peer-reviewed research advancing knowledge in health informatics, educational technology, and human-computer interaction.
Research Areas
Human-Centered and Socio-Technical Innovation
Focuses on human-system interaction, participatory design, and collaborative technologies for health and education. Encompasses usability, user experience, and technological fit in real-world contexts.
Digital Health Transformation and Well-being
Covers outcomes from digital interventions, mobile health, AI-supported care, and data-driven public health approaches. Examines challenges and opportunities for equitable, preventative, and personalized healthcare.
Adaptive Systems and Intelligent Technologies
Highlights research on AI, machine learning, and adaptive digital systems in both education and healthcare settings. Emphasizes responsive, personalized technologies and their real-world deployment.
Ethics, Trust, and Responsible AI
Addresses equity, governance, legal, and ethical challenges in AI adoption, especially in sensitive sectors like health and education. Explores frameworks and best practices for inclusive, transparent, and trustworthy technological development.
Technology Adoption, Literacy, and Impact
Investigates patterns, barriers, and enablers in the uptake and sustained use of digital solutions, from wearables to learning platforms. Includes educational efforts to foster AI literacy and readiness in public health and societal contexts.
Intervention Design, Evaluation, and Outcomes
Focuses on methods for designing, implementing, and evaluating digital interventions for behavior change, health improvement, and learning advancement. Encompasses participatory, stakeholder-driven approaches and impact assessment.
Publications
Journal Articles
Peer-reviewed research published in leading academic journals.
Machine Learning and Thematic Analysis of Suicide Prevention Mobile Applications
This study combines machine learning techniques with qualitative thematic analysis to evaluate the effectiveness and user experience of suicide prevention mobile applications. Findings inform design recommendations for mental health technology.
User Reviews of Depression App Features: Analysis and Implications
Systematic analysis of user-generated reviews to identify key features and pain points in depression management applications. Results highlight the importance of personalization and evidence-based interventions.
MOOCs Users' Concerns in Mobile Learning Applications
Investigation of user concerns and barriers to adoption in mobile learning platforms. Provides insights for improving accessibility and engagement in online education.
Evaluation Framework for Depression Management Applications
Development and validation of a comprehensive evaluation framework for assessing the quality and effectiveness of depression management mobile applications.
Conference Proceedings
Presentations and papers at national and international conferences.
LIWC-Based Sentiment Analysis of Depression Apps
Novel methodology combining Linguistic Inquiry and Word Count (LIWC) with sentiment analysis to evaluate mental health applications.
Insights from Depression App User Reviews
Comprehensive analysis using sentiment analysis and topic modeling to extract actionable insights from user feedback.
Ontological Model for Asthma Diagnosis
Knowledge representation framework supporting clinical decision-making in asthma diagnosis and management.
Access Full Publication List
For a complete list of publications, citations, and research metrics, visit my Google Scholar profile or download my CV.