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Michal Zochowski

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Michal Zochowski is a Professor in the Departments of Physics and Biophysics Program. His research interests lie in the intersection of physics and neuroscience. His group focuses on understanding the mechanisms of the formation of spatio-temporal patterns in coupled dynamical systems, their applicability and role during information processing in the brain. They use theoretical and experimental approaches, including computational modeling of various brain processes including memory storage, consolidation and its retrieval.

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Charles Doering

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Charles Doering is the Nicholas D. Kazarinoff Collegiate Professor of Complex Systems, Mathematics and Physics and the Director of the Center for the Study of Complex Systems. He is a Fellow of the American Physical Society, and a Fellow of the Society of Industrial and Applied Mathematics (SIAM). He uses stochastic, dynamical systems arising in biology, chemistry and physics models, as well as systems of nonlinear partial differential equations to extract reliable, rigorous and useful predictions. His research spans rigorous estimation, numerical simulations and abstract functional and probabilistic analysis.

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Kai Sun

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His  research focuses on understanding the role of strong correction effects in many-body quantum systems. The objective is to discover novel quantum states/materials and to understand their exotic properties using theoretical/numerical methods (with emphasis on topological properties). In his research, numerical techniques are applied to resolve the fate of a quantum material (or a theoretical model) in the presence of multiple competing ground states and to provide quantitative guidance for further (experimental/theoretical) investigations.

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Interaction induced topological insulator with spontaneously-generated orbital rotations. This figure demonstrate how to use strong interactions to generate a topological state of matter in a many-body quantum system.

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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.

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Chris Miller

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Christopher J. Miller is an Assistant Professor of Astronomy and Physics. Professor Miller is a leader in the field of astronomical data mining and computational astrostatistics. He co-founded the INternational Computational Astrostatistics (INCA) group, a collaboration of researchers from the University of Michigan, Carnegie Mellon University, University of Washington, Georgia Tech, the NOAO, and others. From 2008-2010, he led the NOAO Science Data Management group, where he was responsible for using and delivering science quality astronomical data from the US ground-based observatories. He was hired at the University of Michigan under a U-M Presidential initiative for advancing data mining research. His research interests include studies of large-scale structure and cosmology, galaxy clustering, galaxy formation and galaxy evolution.

Astro-informatics is an emerging discipline which matches the large, complex, and time-varying datasets generated by earth and space-based astronomical observatories, to modern unsolved challenges in computer science and statistics. In this example, we compare a semi-blind Fourier deconvolution of an astronomical image (left) to the forward modeling of a physically motivated but smooth galaxy light profile (right). Note that the data are sparse and that the underlying point-spread functions (PSF) are not well known. The technique on the left was developed by Se Un Park (a PhD graduate from EECS) and produces an estimate of the PSF from the data. The method on the right is a traditional astronomical technique. The goal is to obtain the best shape classification of the galaxies in the Universe. With our research, we hope to uncover some of Nature’s astrophysical secrets through the interdisciplinary development and application of computer science algorithms and statistical methods on astronomical datasets.

Astro-informatics is an emerging discipline which matches the large, complex, and time-varying datasets generated by earth and space-based astronomical observatories, to modern unsolved challenges in computer science and statistics. In this example, we compare a semi-blind Fourier deconvolution of an astronomical image (left) to the forward modeling of a physically motivated but smooth galaxy light profile (right). Note that the data are sparse and that the underlying point-spread functions (PSF) are not well known. The technique on the left was developed by Se Un Park (a PhD graduate from EECS) and produces an estimate of the PSF from the data. The method on the right is a traditional astronomical technique. The goal is to obtain the best shape classification of the galaxies in the Universe. With our research, we hope to uncover some of Nature’s astrophysical secrets through the interdisciplinary development and application of computer science algorithms and statistical methods on astronomical datasets.

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Sharon Glotzer

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Sharon Glotzer is a Professor of Chemical Engineering and of Material Science and Engineering. The Glotzer group uses computer simulations to discover the fundamental principles by which nanoscale systems of building blocks self-assemble into higher order, complex, and often hierarchical structures. Their goal is to learn how to manipulate matter at the molecular, nanoparticle, and colloidal scales to create “designer” structures through assembly engineering. Using molecular dynamics and Monte Carlo simulation codes developed in-house for graphics processors (GPUs) and scalable to large hybrid CPU/GPU clusters, they are the leading computational assembly group in the world, with the most powerful codes for studying assembly and packing. Among others, they are the lead developer of HOOMD-Blue, the fastest molecular dynamics code written solely for GPUs and distributed freely as open source software on codeblue.umich.edu.  Based on the fundament scientific principles of assembly gleaned from their studies, they carry out high throughout simulations for materials by design, contributing to the national Materials Genome Initiative.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.

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Emanuel Gull

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Professor Gull works in the general area of computational condensed matter physics with a focus on the study of correlated electronic systems in and out of equilibrium. He is an expert on Monte Carlo methods for quantum systems and one of the developers of the diagrammatic ‘continuous-time’ quantum Monte Carlo methods. His recent work includes the study of the Hubbard model using large cluster dynamical mean field methods, the development of vertex function methods for optical (Raman and optical conductivity) probes, and the development of bold line diagrammatic algorithms for quantum impurities out of equilibrium. Professor Gull is involved in the development of open source computer programs for strongly correlated systems.

Cluster geometry of a 16-site cluster used to study the properties of high-temperature cuprate superconductors.

Cluster geometry of a 16-site cluster used to study the properties of high-temperature cuprate superconductors.

 

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Greg van Anders

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Soft condensed matter involves the understanding of substances with multiple competing interactions, and without clear separation between scales, and leading to an interesting range of phenomena that sit at the boundary of physics, chemistry, and materials science. Professor van Anders works on developing new understanding and control of soft matter, with a primary focus on colloidal systems. His prior work has introduced the concept of “shape entropy” as a universal mechanism to explain the entropy-driven ordering of a wide range of colloidal systems, and the statistical mechanics technique of “digital alchemy” for designing and controlling emergent behaviors in colloids. Professor van Anders is also interested in the use of statistical mechanics to give new insight into design problems in other contexts, such as molecules, materials, and complex systems.

The behavior of hard particles is determined by the emergence of directional entropic forces between pairs of particles. The highlighted pair of tetrahedra have been forced face-to-face to balance their entropy with the rest of the system.

The behavior of hard particles is determined by the emergence of directional entropic forces between pairs of particles. The highlighted pair of tetrahedra have been forced face-to-face to balance their entropy with the rest of the system.

 

 

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R. Paul Drake

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Prof. Drake has played a leading role in the development of two related fields of inquiry — High-Energy-Density Physics (HEDP) and High-Energy-Density Laboratory Astrophysics (HEDLA). This has grown from his scientific work, encompassing experiment, theory, and simulation in several topical areas. His work at Michigan, since 1996, has emphasized hydrodynamics and radiation hydrodynamics with an emphasis on connections to supernovae and other applications to astrophysics.

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August (Gus) Evrard

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August (Gus) Evrard is Arthur F. Thurnau Professor in the departments of Physics and Astronomy, and the Michigan Center for Theoretical Physics. He serves as Associate Director for Community Engagement with ARC. Professor Evrard is a computational cosmologist who models the formation and evolution of large-scale cosmic structure. He currently co-leads the Simulation Working Group for the US-led Dark Energy Survey and is a member of the XMM-XXL project and Virgo Consortium based in Europe. His research uses N-body and hydrodynamic methods to study the formation of galaxies and clusters of galaxies. The simulations also produce synthetic expectations for astronomical sky surveys, providing truth tables that are essential for verifying data handling and statistical processing methods applied to survey data to study the nature of dark matter and dark energy. Professor Evrard was named a Fellow of the American Physical Society in 2011 and an ORCID Ambassador in 2013. He is active in instructional technology at Michigan, founding the Academic Reporting Tool service in use since 2006 and Problem Roulette, a cloud-based study service that offers random, topical access to old exam questions for students in introductory physics classes.

Synthetic sky image derived from N-body simulations of a universe dominated by vacuum energy.

Synthetic sky image derived from N-body simulations of a universe dominated by vacuum energy.