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Steven Skerlos

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Steven Skerlos is an Arthur F. Thurnau Professor of Mechanical Engineering and a Professor of Civil and Environmental Engineering. He is the director of the U-M program in Sustainable Engineering and co-director of the Engineering Sustainable Systems Program. His research focus is on the design of technology systems to reduce environmental impact while advancing economic and societal objectives. His group works on environmental and sustainable technology systems, life cycle product design optimization and sustainable water and wastewater systems, among other topics. From designing humanitarian technologies to purifying water using anaerobic membrane reactors, Prof. Skerlos research addresses challenges in the fields of systems design, technology selection, manufacturing, and water.

Sustainable Technology Policy Maximizing the cost-effectiveness of pollution elimination eastlab.org

Sustainable Technology Policy
Maximizing the cost-effectiveness of pollution elimination (eastlab.org)

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Ming Lin

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Ming Lin’s research focuses on  high dimensional high order statistics and the related applications in real world machine learning problems. His recent research topics includes symmetric matrix sensing, Positive Unlabeled learning, One-bit Active learning and nonconvex tensor machine.

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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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C. David Remy

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C. David Remy is an Assistant Professor of Mechanical Engineering, and head of the Robotics and Motion Laboratory. The lab seeks to systematically exploit mechanical dynamics to make future robots faster, more efficient, and more agile.  Inspired by nature, the group designs and controls robots whose motion emerges in great part passively from the interaction of inertia, gravity, and elastic oscillations, and is merely initiated and shaped through active actuator inputs. In the long term vision, the lab’s research will allow the development of systems that reach and even exceed the agility of humans and animals. It will enable us to build autonomous robots that can run as fast as a cheetah and as enduring as a husky, while mastering the same terrain as a mountain goat. To this end, the group will develop appropriate methods for the control and design of robots. It will draw inspiration from biomechanics and biology, deepen our theoretical understanding of natural dynamics through simulation, and employ advanced numerical optimization as primary tool for systematic design and development.

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Jeff Fessler

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Jeff Fessler is a Professor in the Department of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division. His research interests include numerical optimization, inverse problems, image reconstruction, computational imaging, tomography, magnetic resonance imaging.  Most of these applications involve large problem sizes and parallel computing methods (cluster, cloud, GPU, SIMD, etc.) are needed.

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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.