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Chris Quintana

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Chris Quintana is an Associate Professor in the School of Education. He is a principal investigator in the Center for Highly Interactive Classrooms, Curricula, and Computing in Education. His research is focused on software-based scaffolding for middle school science students, including the development of scaffolded software tools, scaffolding frameworks for software, and learner-centered design processes. He leads the NSF Zydeco Project to explore how web-based technologies and mobile devices can be integrated to connect science classrooms and museums to expand science learning opportunities.

Profesor Quintana holds a Ph. D. in Computer Science from the University of Michigan, and is a member of MICDE’s Education committee whose goal is to continuously review and develop the institute’s educational programs and campus wide teaching of computational sciences, in and out of the classrooms.

Name: Cindy Finelli
Uniqname: cfinelli
Department: ADUE

Photo: Joseph Xu, Michigan Engineering Communications and Marketing
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Cynthia Finelli

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Cynthia Finelli is an Associate Professor in the department of Electrical Engineering and Computer Science, ECE division, and in the School of Education. Her research area is engineering education. She is the Founding Director of the Center for Research and Learning in Engineering at Michigan, which supports college-wide initiatives in engineering teaching and learning, and currently serves as its Faculty Director for Engineering Education Research. Her current research focus on student’s resistance to active learning, faculty adoption of evidence-based teaching practices, student teams in the engineering classroom, and institutional change. Prof. Finelli is a member of MICDE’s Education committee whose goal is to continuously review and develop the institute’s educational programs and campus wide teaching of computational sciences, in and out of the classrooms.

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Kenneth Powell

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Prof. Powell’s work focuses on algorithm development for fluid dynamics, aerodynamics and plasmadynamics, and the application of computational methods to problems in aerodynamics, aeroelasticicty, fluid dynamics and space environment/space weather.

Simulation results for interaction of a solar coronal mass ejection with Earth’s magnetosphere.

Simulation results for interaction of a solar coronal mass ejection with Earth’s magnetosphere.

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Quentin Stout

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Most of his research and teaching involves parallel computing of some form: design of scalable algorithms and data structures; applications to numerous scientific problems such as a large multidisciplinary team modeling space weather or a small interdisciplinary group doing imputation on datasets of social preferences; and performance analysis, both experimental and analytical.  These projects have used a variety of computer architectures, ranging from tens to hundreds of thousands of cores. He also works on algorithms for abstract fine-grain parallel computer models motivated by concerns such as time/number-of-processors/peak-power tradeoffs and the constraints imposed by the fact that computation is done in 2- or 3-dimensional space. Further, he develops serial algorithms for optimizing adaptive sampling problems such as adaptive clinical trials, algorithms for isotonic regression, and various other computer science problems.

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Shravan Veerapaneni

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His research group develops fast and scalable algorithms for solving differential and integral equations on complex moving geometries. Application areas of current interest include large-scale simulations of blood flow through arbitrary confined geometries, electrohydrodynamics of soft particles and heat flow on time-varying domains.

simulation of red blood cells

Snapshot from a hydrodynamic simulation of 40,000 red blood cells with the inset showing the details of a two-body interaction.

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William R. Martin

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Martin’s research involves development of advanced methods for high fidelity analysis of nuclear reactors, with both Monte Carlo methods and deterministic methods. The methods utilize a high-dimensional phase space (7 independent variables) with large data structures that depend nonlinearly on the solution and are big enough to require domain decomposition. Hybrid techniques combining Monte Carlo and deterministic methods yield huge sparse matrices that require innovative storage and inversion algorithms.

Fission source eigenmodes for a 2D full-core, pressurized water reactor using Monte Carlo to estimate the entries of a 2500×2500 fission matrix, a theoretically full but practically sparse matrix of spatial transition rates.

Fission source eigenmodes for a 2D full-core, pressurized water reactor using Monte Carlo to estimate the entries of a 2500×2500 fission matrix, a theoretically full but practically sparse matrix of spatial transition rates.

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Amy Cohn

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Amy Cohn is an Arthur F. Thurnau Professor, Associate Professor of Industrial and Operations Engineering, and of Health Management and Policy, and Associate Director for the Center for Healthcare Engineering and Patient Safety. Her research focus is on developing models and algorithms for solving large-scale applied problems in healthcare, aviation, and other industries. She focuses predominantly on scheduling and resource allocation problems, with an emphasis on solutions that are not only provably optimal (or near-optimal) in theory, but that are implementable and sustainable in practice. This research is highly data-driven and based on deep multi-disciplinary collaborations.

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Krzysztof Fidkowski

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Fidkowski’s research interests lie in the development of robust, scalable, and adaptive solvers for computational fluid dynamics. Target applications include steady and unsteady convection dominated flows, such as those observed in external aerodynamics. Quantitative numerical error estimates for these problems are important for vehicle analysis and design; however they are challenging to obtain, especially for multi-dimensional simulations involving complex physical models running on parallel architectures. Fidkowski’s group is applying adjoint-based error estimation techniques to these problems, with the goal of generating tailored meshes for the prediction of selected outputs of interest. Research topics under investigation include improving effectivity of error estimates, applying error estimation to novel discretizations, combining error estimation with uncertainty quantification and optimization, and diversifying adaptation mechanics, especially for high-order unsteady simulations on deformable domains.

Results of adaptive simulations of a three-dimensional wing undergoing flapping motion in viscous flow. The target output of interest is the lift at the end of the simulation. Tailored meshes are created by increasing the approximation order on selected elements identified by an output-sensitivity error estimate. The resulting output converges much faster in terms of total degrees of freedom used when compared to other adaptive methods, including residual-based adaptation and uniform order refinement.

Results of adaptive simulations of a three-dimensional wing undergoing flapping motion in viscous flow. The target output of interest is the lift at the end of the simulation. Tailored meshes are created by increasing the approximation order on selected elements identified by an output-sensitivity error estimate. The resulting output converges much faster in terms of total degrees of freedom used when compared to other adaptive methods, including residual-based adaptation and uniform order refinement.