Research

Capstone synthesis across ten Engineering-to-Research monographs — tracing the thread from constrained embedded hardware to AI governance, identifying three convergent themes, and mapping a research agenda for explainable, trustworthy AI systems. Formally cites all nine prior volumes by DOI.

Relevance: This is the flagship synthesis that connects all prior research threads to my doctoral work: the journey from embedded systems through security, analytics, and governance converges on explainable AI governance as the discipline that integrates all of them.

AI Governance · Explainable AI · Systems Research

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A research-methods primer for applied computing practitioners transitioning to doctoral work — paradigm selection, mixed-methods design types, a research pipeline model from question formation to contribution, and a conceptual evaluation of evaluation stances.

Relevance: Seeds my dissertation methods chapter: the research pipeline model and the mixed-methods design framework directly inform how I am structuring the empirical components of my doctoral research in AI governance and information technology.

Research Methods · Applied Computing · Academic Research

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A systems engineering primer for security practitioners — security-by-design principles, the cost-quality-schedule trade-off triangle reframed for security, a competency model for secure systems engineers, and companion coursework from ECE 518 and CS 351.

Relevance: Formalizes the systems engineering foundation that underlies my approach to security: security properties are not features added after functional requirements are met — they are constraints that shape the design space from the start.

Security · Systems Engineering · Education

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A four-layer governance-to-control model integrating ERM and cybersecurity policy under Zero Trust — hybrid RBAC-ABAC authorization, NIST CSF 2.0, traceable path from risk appetite to technical control, and a conceptual evaluation against COSO and ISO 27001.

Relevance: Connects enterprise risk management to technical control architecture — the governance layer that sits above individual security controls and below organizational strategy. Central to my doctoral research on AI governance, where policy-to-control traceability is a fundamental design requirement.

Security · Governance · Risk Management

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Examines computer architecture through a security lens — tournament branch predictors, speculative execution side channels, and a hardware-software competency model for security engineers who need to reason below the instruction set.

Relevance: Extends my embedded systems and security background into the hardware-software interface: the coupling thesis — that architectural optimization decisions directly create vulnerability classes — informs how I approach secure system design and AI hardware security research.

Computer Architecture · Security · Systems Research

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A lifecycle-sequenced offensive security curriculum aligned to NICE Framework competencies and Bloom's Taxonomy — covering reconnaissance through post-exploitation with structured learning outcomes, lab progressions, and two pedagogical design principles.

Relevance: Connects my teaching and curriculum development experience with security engineering: structuring how practitioners learn offensive techniques maps directly to how defenders model adversary behavior — a perspective I carry into AI governance and threat-modeling research.

Security · Education · Curriculum Design

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Data mining techniques applied to financial systems — anomaly detection across transaction, market, and operational data, with a unifying interpretability model that explains why the same anomaly means different things in each context.

Relevance: Directly maps to my production work at Genworth: anomaly patterns in derivative pricing and fund accounting data, reconciliation failures as signals, and the regulatory requirement that analytical conclusions be explainable to risk and audit functions.

Data Science · Financial Systems · Machine Learning

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Reference architecture for deploying AI inference across the embedded-to-edge continuum — covering hardware tiers, model compression and quantization, real-time OS constraints, and a security boundary model for edge deployments.

Relevance: Bridges my embedded systems coursework (ECE 442 SensoryPi, AgriEdge) with AI governance research: how to reason about trust, safety, and resource constraints when AI operates at the physical edge of a system.

Embedded Systems · Edge AI · Systems Architecture

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Five-stage data-to-decision workflow bridging MapReduce/Spark infrastructure with feature-first predictive modeling — tree-ensemble models for tabular enterprise data, two case studies.

Relevance: Directly maps to my enterprise data engineering work: pipeline scale, feature engineering discipline, and tree-ensemble modeling for business decision support align with the financial systems and AI governance focus of my doctoral research.

Data Engineering · Data Science · Machine Learning · Enterprise Analytics

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Unified security framework across wireless sensor networks, IoT/CPS, and virtualized infrastructure — common threat taxonomy, cryptographic defense analysis, and three implementation case studies.

Relevance: Bridges my enterprise systems background with doctoral research: threat modeling across layered architectures, post-quantum cryptography, and security engineering for distributed infrastructure.

Security · Networking · Systems Research

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