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Assistant Professor, Department of Systems Engineering and Operations Research
Amissah's research broadly entails an application of concepts and tools from the fields of Modeling & Simulation and Systems Science.Professor, SEOR; Professor and Associate Chair of Undergraduate Studies, Department of Cyber Security Engineering
Research Interests: Cost Modeling, Cyber-Physical Systems, Requirements Engineering, Systems Engineering, Systems ModelingProfessor, Department of Systems Engineering and Operations Research
Research Interests: Information fusion, Bayesian networks, distributed sensor networks, decision making under uncertainty, explainable AIProfessor, Department of Systems Engineering and Operations Research
Research Interests: SimulationProfessor and Chair, Department of Cyber Security Engineering | Director, Center of Excellence in C5I
Research Interests: Probabilistic Ontologies, Decision Support Systems, Cybersecurity, Information Fusion, and Cyber-Physical Systems SecurityAssociate Professor, Department of Systems Engineering and Operations Research
Research Interests: The application of Operations Research to problems related to healthcare and public policyAssistant Professor, Department of Systems Engineering and Operations Research
Research interests: Machine learning and statistical modeling, explainable AI, model fairness and robustness, data exploration, and active learning.Associate Professor, Department of Systems Engineering and Operations Research
Research Interests: Air Transportation, Decision Support Systems, Optimization, Probabilistic Modeling, Simulation, Stochastic ProcessesProfessor and Chair, Department of Systems Engineering and Operations Research
Research Interests: Machine Learning and Data Analytics, Supply Chain Design and Planning, Precision and Sustainable Agriculture, Manufacturing System Design and Modeling, Healthcare and Welfare AnalyticsAssociate Professor, Department of Systems Engineering and Operations Research
Research Interests: Stochastic optimization, distributionally robust optimization, chance-constrained programming, and uncertainty quantification, and data-driven decision-making.