JodyJin

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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Brian Denton

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Brian Denton is a Professor in the Department of Industrial & Operations Engineering, and a member of the Institute for Healthcare Policy and Innovation. His primary research interests are in optimization under uncertainty with applications to medical decision-making. He uses stochastic programming, simulation-optimization and Markov decision processes to optimize decisions regarding detection, treatment, and prevention of chronic diseases, including cancer, diabetes and heart disease.

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Pascal Van Hetenryck

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Pascal Van Hentenryck is the Seth Bonder Collegiate Professor of Industrial & Operations Engineering.

Prof. Van Hentenryck’s research is currently at the intersection of data science and optimization with a focus on risk and resilience, energy systems, transportation, and logistics, marketing, and social networks. Most of these applications require predictive models and optimization over complex infrastructures, natural phenomena, and human behavior.

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Seth Guikema

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Seth Guikema is an Associate Professor of Industrial & Operations Engineering and Civil and Environmental Engineering. Prof. Guikema’s research is focused on and grounded in risk analysis, statistical learning theory, Bayesian probability, stochastic simulation, decision analysis, and agent-based modeling. The issues studied are related to climate adaptation and the sustainability of cities and infrastructure, disaster risk analysis, critical infrastructure modeling, natural hazards, and terrorism risk. Current projects include large-scale agent-based simulation models of evolution of regions in response to repeated climate-related events under different policy scenarios, data-driven evaluation of urban renewal and sustainability, and data-driven predictive modeling of the impacts of storms on power systems.

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Cong Shi

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Cong Shi is an Assistant Professor in the Department of Industrial and Operations Engineering.

Professor Shi’s current research is focused on the design and performance analysis of efficient algorithms for stochastic optimization models, arising in the context of inventory and supply chain management, revenue management, as well as logistics. These stochastic optimization problems involve sequential decision-making under highly evolving or poorly understood environments, which are typically hard to solve to optimality. He constructs efficient heuristics that admit worst-case or average-case performance guarantees, and in doing so he develops novel analytical and computational techniques that are applicable to a broad class of models.

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Ruiwei Jiang

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Ruiwei Jiang is an Assistant Professor in the department of Industrial & Operations Engineering. Prof. Jiang’s research focuses on stochastic optimization and integer programming. He aims to develop data-enabled stochastic optimization (DESO) models and solution methodology that bring together data analytics, integer programming, stochastic programming, and robust optimization. Together with his collaborators, Prof. Jiang applies DESO approaches to various engineering problems, including power and water system operations, renewable energy integration, and healthcare resource scheduling.

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Matthew Plumlee

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Matthew Plumlee’s interests lie in the interface of data and modeling; specifically in methods for experimentation and uncertainty quantification for complex systems. This includes: model calibration; design and analysis of computer experiments; large-scale simulation and experimentation; stochastic modeling of enterprise, mechanical and biological systems; and general analytical/statistical methods and computational techniques.

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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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Siqian Shen

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Prof. Shen’s research derives multifaceted mathematical optimization models for decision making under data uncertainty and information ambiguity. The models she considers often feature stochastic parameters and discrete (0-1) decision variables. The goal is to seek optimal solutions for balancing risk and cost objectives associated with complex systems. She also develops efficient algorithms for solving the large-scale optimization models, based on integer programming, stochastic and data-driven approaches, and special network topologies. In particular, her research has been applied to cyberinfrastructure design and operations management problems related to power grids, transportation, and Cloud Computing systems.

A sensor monitored network for research allocation and routing in highly uncertain environments (e.g., post-disaster delivery, highly congested traffic system, or high-demand computing network). The network is structured by solving a general mathematical optimization model.

A sensor monitored network for research allocation and routing in highly uncertain environments (e.g., post-disaster delivery, highly congested traffic system, or high-demand computing network). The network is structured by solving a general mathematical optimization model.

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Jon Lee

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Professor Lee’s research focus is on nonlinear discrete optimization (NDO). Many practical engineering problems have physical aspects which are naturally modeled through smooth nonlinear functions, as well as design aspects which are often modeled with discrete variables. Research in NDO seeks to marry diverse techniques from classical areas of optimization, for example methods for smooth nonlinear optimization and methods for integer linear programming, with the idea of successfully attacking natural NDO models for practical engineering problems.

A small example illustrating an outer-approximation algorithm for a mixed-integer nonlinear optimization problem.

A small example illustrating an outer-approximation algorithm for a mixed-integer nonlinear optimization problem.