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Heather Mayes

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Heather Mayes is an Assistant Professor in the Department of Chemical Engineering. Her research group uses multi-scale modeling to discover protein-sugar interactions and to harness them for renewable energy and improved health. The study of carbohydrate-protein interactions is an important step to create renewable fuels and chemicals from non-food biomass, and the results can be applied to several human diseases, including cancer and autoimmune disorders. Prof. Mayes uses computational tools in her research, including quantum mechanics, molecular dynamics, and rare-event sampling methods. She collaborates with experimental groups to understand past and guide future wet-lab studies to advance renewable chemicals and fuels, as well as disease understanding.

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Multiscale simulation to uncover mechanisms behind protein-sugar interactions, such as how the T. reesi Cel6A enzyme coordinates making and breaking four bonds for cellulose hydrolysis.

 

 

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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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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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Michael Wellman

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In a strategic environment, agents face decisions where the outcomes depend on the behavior of other autonomous agents. The strategic reasoning group develops techniques for understanding and engineering complex multiagent environments, using concepts and methods from economics as well as computer science. Specifically, we apply game-theoretic principles to data from large-scale agent-based simulation, in an approach called empirical game-theoretic analysis (EGTA). EGTA combines simulation, machine learning, and other empirical methods to reason about the strategic issues in complex multiagent settings. We are particularly interested in domains characterized by dynamism, networks, and uncertainty, including applications in financial markets, information security, and sustainable transportation.

The iterative empirical game-theoretic analysis process.

The iterative empirical game-theoretic analysis process.

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Eric Michielssen

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Eric Michielssen is a Professor of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division and Associate Vice President for Advanced Research Computing.

His research interests include all aspects of theoretical, applied, and computational electromagnetics, with emphasis on the development of fast (primarily) integral-equation-based techniques for analyzing electromagnetic phenomena. His group studies fast multipole methods for analyzing static and high frequency electronic and optical devices, fast direct solvers for scattering analysis, and butterfly algorithms for compressing matrices that arise in the integral equation solution of large-scale electromagnetic problems.

Furthermore, the group works on plane-wave-time-domain algorithms that extend fast multipole concepts to the time domain, and develop time-domain versions of pre-corrected FFT/adaptive integral methods.  Collectively, these algorithms allow the integral equation analysis of time-harmonic and transient electromagnetic phenomena in large-scale linear and nonlinear surface scatterers, antennas, and circuits.

Recently, the group developed powerful Calderon multiplicative preconditioners for accelerating time domain integral equation solvers applied to the analysis of multiscale phenomena, and used the above analysis techniques to develop new closed-loop and multi-objective optimization tools for synthesizing electromagnetic devices, as well as to assist in uncertainty quantification studies relating to electromagnetic compatibility and bioelectromagnetic problems.

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Electromagnetic analysis of computer board and metamaterial.

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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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Ann Jeffers

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Ann Jeffers is an Associate Professor in the Department of Civil and Environmental Engineering. Her research seeks to use computational methods to study structural performance under fire hazards. Jeffers’ work has particularly focused on coupling a high-resolution CFD fire model to a low resolution structural model to study structural performance under natural fire effects. The coupled fire-structure simulation has necessitated the formulation of novel finite elements and algorithms to bridge the disparities between the fire and structural domains. She has also conducted research using probabilistic methods (i.e., Monte Carlo simulation and analytical reliability methods) to study the propagation of uncertainty and evaluate the reliability of structural systems threatened by fire.

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Derek Posselt

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Derek J. Posselt is a Deputy Principal Investigator of the NASA CYGNSS EV-2 Mission. He is an sponsored Affiliate of U-M Climate and Space Sciences and Engineering. His research seeks to quantify the multi-scale interactions that govern the feedback response of dynamically organized cloud systems to changes in the Earth’s climate. It is designed to capitalize on the convergence between modern computing resources, global observing systems, and nonlinear ensemble-based data assimilation methods. Posselt uses large-domain high-resolution numerical simulations to simultaneously resolve global and local atmospheric processes. He mines datasets collected by in-situ and remote sensing observing systems for information on the Earth’s hydrologic cycle. Posselt generates ensembles of millions of individual numerical simulations to estimate the envelope of uncertainty in projections of Earth’s future climate. Each of these efforts is not only computationally demanding, but also data-intensive, and depends critically on the availability and efficient use of large-capacity computational resources.

Climate visualization

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Karthik Duraisamy

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Prof. Duraisamy is interested in the development of computational models, algorithms and uncertainty quantification approaches with application to fluid flows. This research includes fluid dynamic modeling at a fundamental level as well as in an integrated system-level setting. An overarching theme in his research involves the use of simulation and data-driven methods to answer scientific and engineering questions with an appreciation of the effect of modeling uncertainties on the predicted results. Prof. Duraisamy’s group is also interested in developing numerical algorithms to  operate on evolving computational architectures such as GPUs. He is the Director of the Center for Data-Driven Computational Physics.

Combustion in a turbulent boundary layer.