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Research Methodologies

Optimization

Optimization theory is a building block of operations research and data science. It combines disciplines of algebra, geometry, analysis, combinatorics, probability, statistics, and computer science for data-driven decision-making in complex systems. Our methodological research in optimization spans theory, analysis, and design of computationally efficient, robust, and scalable algorithms to handle real-world problems in engineering, operations, economics, and business.


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Statistical Modeling and Learning

Statistical modeling and learning use statistical models and assumptions to translate complex real-world problems into tractable structures so that predictions about uncertain outcomes or prescriptions to design systems can be made. Going from data to models, our methodological research in this area is shaped around devising novel frameworks that can lead to fair and interpretable decisions and insights. Key drivers to our fundamental research in this area are the explosion in the availability of data and computational powers in the last decade.


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Stochastic Modeling and Systems

Stochastic modeling is built upon probability theory, statistics, and stochastic processes to address uncertain, complex physical, cyber, and service systems. Our fundamental research in this area includes modeling systems, analyzing the system performance with respect to input uncertainty, and validating output against real-world uncertain outcomes. The research in IE spans a whole host of applications in supply chains and healthcare systems. 


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Task Analysis

The Center for Ergonomics operates within the Department of Industrial and Operations Engineering and is dedicated to gaining and sharing a better understanding of how tools, technologies, and work practices affect health and performance and how they can be improved through human-centered design.


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Naturalistic Decision Making

Methods include observing how humans operate and adapt to varying situations in complex naturalistic environments in various domains such as healthcare, education, and crisis response. Observations are augmented by interviews that use knowledge elicitation techniques to capture perspectives of domain actors, ranging from frontline personnel and senior leadership. A key theme of focus is identifying adaptive patterns and emergent properties of systems through proactive learning. Insights from such learning can be used to inform decisions at various organizational levels, including policies, process design, schedules, and resource management.


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