Dr. Funke Oladimeji
Assistant Professor
Office: Harbor Walk West
Phone: 843.953.2824
Email: oladimejioo@cofc.edu
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Curriculum Vitae
Research
- Research Interests-
My research focuses on applied systems engineering for complex socio-technical and cyber-physical systems, with an emphasis on integrating model-based systems engineering (MBSE), artificial intelligence (AI), and system dynamics (SD) to improve system performance, decision-making, and resilience. I am particularly interested in developing structured, verifiable, and scalable approaches for embedding intelligent capabilities into engineering systems while maintaining safety, transparency, and operational reliability The software(s) that I use for modeling can be found on my home page.Core Research Areas
1. AI-Integrated Model-Based Systems Engineering (MBSE)
This research explores how artificial intelligence can be systematically governed, integrated, and validated within MBSE frameworks. The key topics explored include:
a. AI as a bounded and verifiable component in engineering systems
b. AI–PLC interface architectures for real-time control systems
c. Requirements, constraints, and verification of AI-enabled decisions
d. Use of SysML for traceability between AI models and system behavior
This work addresses a critical gap in engineering practice while AI enhances decision-making, it often lacks formal integration and governance within system architectures.
2. System Dynamics for Organizational and System Performance
This research uses system dynamics to model and analyze including performance measurement systems (PMS), Organizational resilience and feedback-driven implementation processes. The work focuses on identifying:
a. feedback structures (reinforcing and balancing loops)
b. leverage points for performance improvement
c. dynamic relationships between leadership, culture, and system outcomes
The goal is to move beyond static analysis and toward dynamic, simulation-based decision support.
Figure 2: An example of a causal loop diagram (source: Oladimeji, O., *Macanas, J., *Michaelson, R, (2025). Addressing the Systems Engineering Skills Gap with Industry Demands Using System Dynamics. In Proceedings of the American Society for Engineering Management (ASEM)).
3. Industrial Automation and Cyber-Physical Systems (PLC-Based)
This area focuses on the design and analysis of real-world control systems, particularly:
a. PLC-controlled electromechanical systems
b. Industrial communication (e.g., Profinet, OPC UA)
c. Integration of AI into deterministic control environments
d. Real-time constraints, safety, and fault handling
A key contribution is bridging traditional industrial automation to modern AI-enabled systems engineering.
4. Digital Twins and Decision Intelligence
This research investigates how digital twins and AI-driven models can support:
a. Real-time monitoring and prediction
b. Scenario-based analysis and system optimization
c. Data-driven decision-making in complex systems
Digital twins act as a virtual testbed, enabling safe experimentation, performance evaluation and adaptive system behavior. This aligns with emerging approaches where AI and simulation are combined to enhance decision intelligence in engineering systems.
My research integrates multiple complementary methodologies:
• Model-Based Systems Engineering (MBSE) for structured system representation
• System Dynamics (SD) for feedback and time-dependent behavior
• Statistical and causal modeling (e.g., SEM) for empirical validation
• Multi-criteria decision methods (e.g., ANP) for complex trade-off analysis
These methods are are combined to create unified modeling and decision frameworks.
The long-term goal of my research is to develop a unified, model-based framework for designing, governing, and optimizing intelligent engineering systems. This includes:
• embedding AI within engineering systems in a safe, explainable, and verifiable way
• linking organizational performance models with technical system design
• enabling data-driven, real-time decision-making through integrated digital models
If you're interested in this research or might be interested in joining me in research, contact me. I welcome collaborations in:
• AI-enabled systems engineering
• Digital twin development
• Industrial automation and control systems
• Engineering education and workforce development
I'll be glad to talk to you about my ongoing research.