Dr. T. Steven Cotter

Biography

T. Steven Cotter (BS, MBA, MS, PhD) is a Senior Lecturer with the Engineering Management and Systems Engineering department at Old Dominion University. He is a Certified Quality Engineer and Certified Reliability Engineer with the American Society for Quality. He holds certifications as a SQL Server Application Developer and Database Administrator. He has over 40 years of experience in quality and production engineering and management in automated chemicals manufacturing, automated computer manufacturing, and defense electronics. He has supported projects for the startup of two production facilities and a research and development center. He has managed projects for Six Sigma quality improvement, automated quality information systems, and automated measurement systems. He lectured for five years as an Adjunct Assistant Professor before joining the Department of Engineering Management and Systems Engineering as full time faculty. His research initiatives are in computational statistics engineering, computational quality and reliability engineering, and socio-intelligent-machine systems.

Education

Ph.D. Engineering Management and Systems Engineering, 2005

Old Dominion University

M.S. Engineering Managment, 1994

Concentration: Quality and Reliability Engineering

University of Massachusetts at Amherst

M.B.A., 1989

Concentration: Finance

BS, 1986

Concentration: Management

University of South Carolina

Electronic Technology, 1971

Graff Area Vocational and Technical School

(now Ozarks Technical Community College)

Professional Associations

American Society for Engineering Management

American Society for Quality

• Certified Quality Engineer

• Certified Reliability Engineer

Association for Computing Machinery

INFORMS

International Statistical Engineering Association

Courses Taught

ENMA 302, Engineering Economics

ENMA 401, Project Management

ENMA 420/520, Statistical Concepts in Engineering Management

ENMA 605 Capstone Project

ENMA 614, Quality Systems Design

ENMA 711/811, Methodology for Advanced Engineering Projects

ENMA 720/820, Multivariate Analysis for Engineering Research

ENMA 897 Special Topics Courses

* Applied Ontology Engineering

* Hybrid Ai-Human Systems Control

* Psychology of Judgment and Decision Making

* Quality Systems Risk Engineering

* Software Quality Engineering

Research Areas

Computational Systems Statistical Engineering: How can deterministic and stochastic engineering models be integrated into state space hierarchical Bayesian models of structural and stochastic components to provide dynamic systems solutions? Application of computational methods to the study, design, operation, and management of complex system of systems through the integration of mechanistic and stochastic engineering models within state space causal Bayesian hierarchical models of systemic structural and stochastic components to provide dynamic systems solutions. Objective - Develop a general theory of stochastic systems engineering that integrates deterministic engineering causal models and stochastic models within a state space causal Bayesian framework.

AI-Human Quality and Reliability Systems: What systemic human-machine intelligence structures, processes, state-space control structures, and information/knowledge systems yield optimally cost-effective quality-reliability systems? Integration of AI-Human socio-machine-intelligence into existing quality and reliability theory and application to dynamically model and design human-machine intelligence structures, processes, state space control structures, and information systems that yield optimally cost effective 21st century quality / reliability systems. Objective - Extend the American Society for Quality Certified Manager of Quality/Organizational Excellence body of knowledge to integrate human-machine economic effectiveness and quality/reliability systems effectiveness into organizational effectiveness.

AI-Human Knowledge Engineering: What decision governance/-management theories, policies, procedures, and cybernetic control structures are needed for the integration and management of human-intelligence/machine-intelligence in 21st century socio-intelligent-machine systems? Integration of knowledge engineering theory, artificial intelligence, and computational engineering methods to the development of decision governance management theories, policies, procedures, and cybernetic control structures needed for the integration and management of of 21st century socio-intelligent-machine systems. Objective - Understand the impact that human-machine intelligence integration will have on the management of organizational engineering functions in the 21^st century.

Digital Engineering Informatics. How can artificial intelligent systems be integrated with human-computer systems over remote and local networks to manage the computational, informational, and knowledge digital-artefacts generated in the design/development-deploy/scale-up-implement/scale-out-decommission/scale-down engineering workflow? Objective - Integrate advances in analytics, artificial intelligence, computational engineering, information systems, and cyber-security within the engineering design workflow to develop Digital Engineering Informatics as an academic and professional discipline.

Teaching Philosophy

Coming from an industrial engineering, managerial, and teaching background, I view students as both my customer and product. My objectives in interacting with, mentoring, and teaching students are to facilitate their transition into an integrated management, engineering, and technology education and to support their maturation through that education into functioning professional technicians, engineers, and engineering managers who represent their school and chosen disciplines with the highest degree of integrity and competence. I support student transition and maturation through a mentoring Socratic teaching style supplemented with learning experiences including sequenced problem analysis and solution activities, case studies, and research, documentation, and presentation with defense. I use the Socratic Method in lecture, questioning, and testing to guide students toward a greater understanding of the required engineering or technical knowledge. I use learning experiences as a discovery process to support greater understanding and increased performance. As a former industrial manager, I set the expectation that students contribute as much effort in attaining managerial, engineering, or technical education as I do in providing it.