Victoria Booth

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Victoria Booth is an Associate Professor in the Department of Mathematics and the Department of Anesthesiology. Her interdisciplinary research in mathematical and computational neurosciences focuses on constructing and analyzing biophysical models of neurons and neural networks in order to quantitatively probe experimental hypothesis and provide experimentally-testable predictions. Her research provides continuous reciprocal interactions between modeling and experimental results.

Prof. Booth and her colleagues are constructing neurophysiologically based models of the neuronal networks and neurotransmitter interactions in the brainstem and the hypothalamus that regulate wake and sleep states. She is also addressing the question of the influence of intrinsic neuron properties and network topology on the generation of spatio-temporal activity patterns in large-scale neural networks.

C. Alberto Figueroa

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Alberto Figueroa is an Associate Professor with a joint appointment in Biomedical Engineering and Surgery. He works on computational methods for patient-specific cardiovascular simulation.figueroa_image-264x300

Modeling the function of the cardiovascular system in health and disease represents a fascinating scientific challenge. This challenge can only be addressed by combining a deep understanding of the physiologic, biologic, engineering and mathematical principles involved.Our lab uses medical image processing, high performance computational fluid dynamics simulation, and multi-scale modeling to simulate blood flow in the human body. Our specific areas of interest are surgical planning, disease research, arterial growth and remodeling, and medical device design and performance evaluation.

Barzan Mozafari

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Barzan Mozafari is an Assistant Professor of Electrical Engineering and Computer Science at the University of Michigan (Ann Arbor), where he is a member of the Michigan Database Group and the Software Systems Lab. Prior to that, he was a postdoctoral associate at Massachusetts Institute of Technology. He earned his Ph.D. in Computer Science from the University of California at Los Angeles. He is passionate about building large-scale data-intensive systems, with a particular interest in database-as-a-service clouds, distributed systems, and crowdsourcing. In his research, he draws on advanced mathematical models to deliver practical database solutions. He has won several awards and fellowships, including SIGMOD 2012 and EuroSys 2013’s best paper awards.

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.

Paul Zimmerman

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From elementary chemical reactions to exciton dynamics in solar cells, chemistry is a particularly rich field for atomistic simulation. Research in the Zimmerman group develops and employs a broad spectrum of computational techniques to chemical problems. Special emphasis is taken on creating new, practical computational methods for application to problems that are considered out-of-reach to standard simulation methodologies. For instance, automated prediction of chemical reactions has long been considered impossible using quantum chemical simulation. To break this limitation, the Zimmerman group is creating new techniques for locating reaction paths and products of catalytic reactions, with the goal of predicting the outcome of reactions prior to experiment. These tools use a combination of chemical intuition, applied mathematics, and massively parallel computation to achieve an impressive level of automation and predictive value.

Automatically generated growth pattern of a chemical reaction network involving a hydrogen storage material, NH3BH3.

Automatically generated growth pattern of a chemical reaction network involving a hydrogen storage material, NH3BH3.

Christiane Jablonowski

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Christiane Jablonowski is an Associate Professor in the Department of Climate and Space Sciences and Engineering. Her research is highly interdisciplinary and combines atmospheric science, applied mathematics, computational science and high-performance computing. Her research suggests new pathways to bridge the wide range of spatial scales between local, regional and global phenomena in climate models without the prohibitive computational costs of global high-resolution simulations. In particular, she advances variable-resolution and Adaptive Mesh Refinement (AMR) techniques for future-generation weather and climate models that are built upon a cubed-sphere computational mesh. Variable-resolution meshes enable climate modelers to focus the computational resources on features or regions of interest, and thereby allow an assessment of the many multi-scale interactions between, for example, tropical cyclones and the general circulation of the atmosphere.

Dr. Jablonowski organizes summer schools, dynamical core model intercomparison projects, teaches tutorials on parallel computing and climate modeling, develops cyber-infrastructure tools for the climate sciences, and has co-edited and co-authored a book on numerical methods for atmospheric models.

Snapshot of a 2D atmospheric model simulation showing a developing wave that is dynamically tracked by a block-structured and adaptive cubed-sphere computational mesh. Blue and red colors denote a clockwise and counterclockwise rotational motion, respectively.

Snapshot of a 2D atmospheric model simulation showing a developing wave that is dynamically tracked by a block-structured and adaptive cubed-sphere computational mesh. Blue and red colors denote a clockwise and counterclockwise rotational motion, respectively.

Eric Johnsen

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His research interests lie in the development of numerical methods and models for massively parallel computations of fluid mechanics problems on modern computing architectures, including GPUs. He specifically focuses on high-order accurate finite difference/volume/element and spectral methods desgined for robust, accurate and efficient simulations. With his codes, he investigates the basic physics of multiphase flows, high-speed flows and shock waves, turbulence and mixing, interfacial instabilities, complex fluids and plasmas. Target applications include biomedical engineering, energy, aeronautics and naval engineering.

Time sequence showing turbulent mixing between two fluids of different densities. Vortical structures colored by density highlight how the mixing region grows while the turbulence decays. The results are obtained using direct numerical simulation (DNS), in which all dynamical scales are resolved.

Time sequence showing turbulent mixing between two fluids of different densities. Vortical structures colored by density highlight how the mixing region grows while the turbulence decays. The results are obtained using direct numerical simulation (DNS), in which all dynamical scales are resolved.

Robert Krasny

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His research goal is to develop accurate and efficient numerical methods for computational problems in science and engineering. The methods he works on typically use the Green’s function to convert the relevant differential equation into an integral equation. Krasny develops treecode algorithms for efficient computation of long-range particle interactions. Topics of interest include fluid dynamics (vortex sheets, vortex rings, Hamiltonian chaos, geophysical flow), and electrostatics (Poisson-Boltzmann model for solvated proteins). He is also interested in modeling charge transport in organic solar cells.

This picture illustrates the instability of a vortex ring. The ring was modeled as a circular disk vortex sheet with an imposed perturbation of azimuthal wavenumber m=8. The ring’s motion was computed using a Lagrangian particle method and a treecode algorithm for fast evaluation of the induced velocity. The picture shows three isosurfaces of vorticity at a late time in the simulation. The results reveal details of the instability, in particular the relation between axial flow and collapse of the vortex core.

This picture illustrates the instability of a vortex ring. The ring was modeled as a circular disk vortex sheet with an imposed perturbation of azimuthal wavenumber m=8. The ring’s motion was computed using a Lagrangian particle method and a treecode algorithm for fast evaluation of the induced velocity. The picture shows three isosurfaces of vorticity at a late time in the simulation. The results reveal details of the instability, in particular the relation between axial flow and collapse of the vortex core.

Shawn McKee

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Shawn McKee is a Research Scientist in the Department of Physics, and the Director of MICDE’s Center for Network and Storage-Enabled Collaborative Computational Science.

He is also the U-M site director for ATLAS Great Lakes Tier 2, which provides 4,000 CPUs cores and 3.5 petabytes of storage for ATLAS physics computing. McKee’s research interests are mainly in two parts: using the ATLAS detector to search for Dark-Matter (assuming it has a particle physics origin; and researching distributed data-intensive infrastructures to improve their ability to support high-energy physics and similar distributed e-Science efforts.

A “gold-plated” Higgs -> 4 muon decay at 124.5 GeV from July 10, 2012 displayed in the ATLAS 3D event viewer.

A “gold-plated” Higgs -> 4 muon decay at 124.5 GeV from July 10, 2012 displayed in the ATLAS 3D event viewer.

Vikram Gavini

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His research group aims to develop computational and mathematical techniques to address various aspects of materials behavior, which exhibit complexity and structure on varying length and time scales. The work draws ideas from quantum mechanics, statistical mechanics and homogenization theories to create multi-scale models from fundamental principles, which provide insight into the complex behavior of materials. Topics of research include developing multi-scale methods for density-functional theory (electronic structure) calculations at continuum scales, electronic structure studies on defects in materials, quasi-continuum method, analysis of approximation theories, numerical analysis, and quantum transport in materials.

Hierarchy of triangulations that form the basis of a coarse-graining methods (quasi-continuum reduction) for conducting electronic structure calculations at macroscopic scales.

Hierarchy of triangulations that form the basis of a coarse-graining methods (quasi-continuum reduction) for conducting electronic structure calculations at macroscopic scales.