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Judy Jin

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Judy Jin is a Professor in the Department of Industrial & Operations Engineering and the Director of the Manufacturing Engineering Program of the Integrative Systems and Design Division. Her research focuses on data fusion and system informatics for better comprehension and operation of engineering systems and decision-making for quality and reliability assurance. Her research is applied in several fields, including energy, manufacturing, medical decision making, telecommunications, transportation and unmanned ground vehicle (UGV).

Jody Jin

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Michael Cafarella

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Michael Cafarella is an Associate Professor in the Department of Electrical Engineering and Computer Science, Computer Science Division. He was appointed the Morris Wellman Faculty Development Assistant Professor of Computer Science and Engineering, and a Sloan Research Fellow (2016). Prof. Cafarella studies databases, information extraction, data integration, and data mining. His projects span several areas of data management including systems and algorithms for “messy” data management, novel data-intensive applications, and data systems infrastructure.

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Henry Lam

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His  research focuses on several methodological aspects of simulation modeling and its intersection with optimization and statistics. He is interested in quantifying and mitigating the uncertainty of stochastic performance analysis due to model misspecification, by developing simulation-based machineries for sensitivity and robust analysis. He is also interested in building computation and optimization strategies that blend with input data, their statistical models, and other information such as output validation data, with the goal of offering guarantees in both computational and statistical efficiency. His other lines of work consist of designing techniques to speed up Monte Carlo schemes and simulation optimization algorithms that are applied to risk management, extreme event analysis, and decision-making in service and engineering systems.

This graph shows how the provably best nonparametric worst-case bounds for the overload probability of a queueing system compare to those using various parametric models on the service time distribution. Generating these bounds requires the use of nested simulation and bootstrapping. The bounds can be used as sensitivity tools to measure the risk of adopting misspecified service time model.

This graph shows how the provably best nonparametric worst-case bounds for the overload probability of a queueing system compare to those using various parametric models on the service time distribution. Generating these bounds requires the use of nested simulation and bootstrapping. The bounds can be used as sensitivity tools to measure the risk of adopting misspecified service time model.

 

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Carol Menassa

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Carol C. Menassa is an Associate Professor and Tishman Faculty Scholar with the Intelligent and Sustainable Civil Infrastructure Systems Laboratory (ISciS Lab) in the Department of Civil and Environmental Engineering.

Current research in Intelligent and Sustainable Civil Infrastructure Systems Laboratory (ISciS Lab) focuses on understanding and modeling the impact of occupants on energy use in buildings, and developing decision frameworks to assist in building operations and management; as well as, sustainable retrofit decisions. The research team used several tools such as energy simulation, complex adaptive systems modeling, high-level architecture and informatics.