Research
Publications & Projects
My research centers on ethical AI and predictive analytics. building machine learning systems that are accurate, fair, and human-centered. A core thread running through all my work is the question of how humans experience, trust, and are impacted by algorithmic systems.
Focus Areas
Research Interests
Ethical & Responsible AI
- Algorithmic fairness & bias mitigation
- Transparency and explainability in ML
- Accountability frameworks for AI systems
- Societal impact of automated decisions
Predictive Analytics & ML
- Supervised & unsupervised learning models
- People analytics & workforce prediction
- Behavioral modeling and pattern detection
- Python, scikit-learn, ML pipelines
Human Factors in AI Systems
- Human-AI interaction & trust
- Cross-cultural technology adoption
- User perception of algorithmic outputs
- Human-centered design for AI
Applied Research & Education
- Intelligent & adaptive learning systems
- AI curriculum design & pedagogy
- Mixed-methods research design
- Translating ML research into practice
Publications
Selected Works
Human Factors in Cybersecurity: A Cross-Cultural Study on Trust
Doctoral dissertation applying mixed-methods research and predictive modeling to examine how cultural context shapes trust and behavioral responses to digital threats. Identifies cross-cultural patterns in human perception of algorithmic and security systems, with implications for the design of ethical, culturally-aware AI and security technologies.
Mapping the Landscape of Industrial Control Systems Cybersecurity: A Delphi Study
Developed a comprehensive cybersecurity concept map for Industrial Control Systems using a Delphi methodology with domain experts. A structured research effort connecting technical knowledge mapping with human factors: examining how expertise is defined, communicated, and developed in high-stakes technical environments.
Curriculum Guidance Document: Industrial Control Systems Cybersecurity
Published through the Center for Education and Research in Information Assurance and Security (CERIAS) at Purdue. Provides context for a scalable national network of cybersecurity institutes utilizing hub-and-spoke model locations in FEMA Region 5, with a plan for developing shareable ICS curriculum across institutions.
SecTutor: An Intelligent Tutoring System for Secure Programming
Presents SecTutor, an adaptive online learning tool for secure programming that tailors educational pathways based on learner assessments. The system identifies knowledge gaps and suggests personalized resources to scaffold skill development. Presented at the IFIP World Conference on Information Security Education (WISE 2022), pp. 17-28.
Toward Proactive Cybersecurity: A Cultural Risk Profiling Framework for Predictive Cybersecurity Analytics
Proposes a cultural risk profiling framework that integrates Hofstede's cultural dimensions with trust factors to improve predictive cybersecurity analytics. Argues that most predictive models treat human behavior as universal, missing how culture shapes risk perception, trust, and security responses. Presents a focused research agenda for developing culturally-aware, ethical predictive models in real-world cybersecurity systems.
XGBoost-Powered Predictive Analytics for Early Identification of Thermal Runaway in Lithium-Ion Batteries
Applies XGBoost-based predictive modeling to detect early-stage thermal runaway in lithium-ion batteries, a critical safety challenge in electric vehicles and energy storage. Demonstrates how machine learning can be deployed to improve real-time safety monitoring, with implications for responsible AI in high-stakes engineering applications.
Find My Work
Academic Profiles
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