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SUMMARY:MICDE Seminar: Amanda Randles\, Department of Biomedical Engineering\, Duke University
DESCRIPTION:Bio: Amanda Randles is an assistant professor of Biomedical Engineering at Duke University. She has courtesy appointments in the departments of Mechanical Engineering and Material Science\, Computer Science and Mathematics\, and is a member of the Duke Cancer Institute. She got her Ph.D. from Harvard University in 2013\, and has been the recipient of the Lawrence Fellowship (Lawrence Livermore National Lab.)\, the Anita Borg Memorial Scholarship (Google)\, and the George Michael Memorial High Performance Computing Fellowship (ACM/IEEE) among many accomplishments in her early career. Her research in biomedical simulation and high performance computing focuses on the development of new computational tools that she uses to provide insight into the localization and development of human diseases ranging from atherosclerosis to cancer. \nMassively Parallel Simulations of Hemodynamics in the Human Vasculature\nThe recognition of the role hemodynamic forces have in the localization and development of disease has motivated large-scale efforts to enable patient-specific simulations. When combined with computational approaches that can extend the models to include physiologically accurate hematocrit levels in large regions of the circulatory system\, these image-based models yield insight into the underlying mechanisms driving disease progression and inform surgical planning or the design of next generation drug delivery systems. Building a detailed\, realistic model of human blood flow\, however\, is a formidable mathematical and computational challenge. The models must incorporate the motion of fluid\, intricate geometry of the blood vessels\, continual pulse-driven changes in flow and pressure\, and the behavior of suspended bodies such as red blood cells. In this talk\, I will discuss the development of HARVEY\, a parallel fluid dynamics application designed to model hemodynamics in patient-specific geometries. I will cover the methods introduced to reduce the overall time-to-solution and enable near-linear strong scaling on up to 1\,572\,864 core of the IBM Blue Gene/Q supercomputer. Finally\, I will present the expansion of the scope of projects to address not only vascular diseases\, but also treatment planning and the movement of circulating tumor cells in the bloodstream. \nProf. Randles is being hosted by Dr. Carrasco-Teja (MICDE). If you would like to meet her during her visit please send an email to mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-amanda-randles-duke-university/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=America/Detroit:20180406T150000
DTEND;TZID=America/Detroit:20180406T160000
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SUMMARY:AIM Seminar: Christoph Börgers\, Mathematics\, Tufts University
DESCRIPTION:Bio: Christoph Börgers is a Professor of Mathematics at Tufts University. He got his Ph.D. under Prof. Charles Peskin at the Courant Institute of Mathematical Sciences\, in 1985. Prof. Börgers was a professor in the University of Michigan department of Mathematics until 1996 when he moved to Tufts. His expertise is in mathematical neuroscience\, applied dynamical systems\, numerical analysis\, scientific computing\, and during the past decade\, most of his work has been in the area of Computational Neuroscience. \nRhythms in neuronal networks with recurrent excitation\nInteracting excitatory and inhibitory neuronal populations often generate oscillations in electrical fields in the brain. I will briefly review this mechanism and the reasons to believe that it is important in brain function. Most of the talk will be focused on the effects of recurrent excitation\, i.e.\, of the neurons of a local network in the brain exciting each other. Recurrent excitation can sustain activity in a network that would otherwise be quiescent; this is believed to be the basis of working memory. It can also lead to a runaway process\, with excitation generating more excitation etc.\, much as the presence of a quadratic term on the right-hand side of a differential equation can lead to blow-up in finite time; this may be related to epileptic seizures. For model problems\, we prove that abrupt transitions to runaway activity require recurrent excitation with fast kinetics\, while working memory activity is more robust with recurrent excitation with slow kinetics. \nProf. Börgers is being hosted by Prof. Robert Krasny (Mathematics).
URL:https://micde.umich.edu/event/aim-seminar-christoph-borgers-mathematics-tufts-university/
LOCATION:1084 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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SUMMARY:MICDE Seminar: Vladimir Druskin\, Scientific Advisor\, Schlumberger Doll Research
DESCRIPTION:Bio: Vladimir Druskin is an applied mathematician with expertise in several areas including numerical algorithms\, large scale numerical simulations\, computational linear algebra\, inverse problems\, model reduction\, computational geophysics\, subsurface and medical imaging\, electrical engineering and financial mathematics. Dr. Druskin got his Ph.D. from Lomonosov Moscow State University (MSU) focused on applied mathematics. He is currently a scientific advisor at Schlumberger Doll Research working in energy research and development with demonstrated successful history of leading large collaborative industrial-academic projects in mathematical modeling and data-processing. \nReduced order models\, networks\, and applications to modeling and imaging with waves\nGeophysical seismic exploration\, as well as radar and sonar imaging require the solution of large-scale forward and inverse problems for hyperbolic systems of equations.  In this talk\, I will show how model order reduction can be used to address some intrinsic difficulties of these problems.  In model order reduction\, one approximates the response (transfer function) of a large-scale dynamical system using a smaller system\, called the reduced order model (ROM).  We consider ROMs that capture properties of the large problem that are essential for imaging and that can be realized via sparse graph-Laplacian networks.  The ROMs are data-driven\, i.e.\, they learn the underlying PDE problem from the transfer function.  One of the better-known applications of our ROMs is the efficient discretization of PDE problems in unbounded domains.  Here I will focus on two recent applications: (i) Multiscale modeling of elastic wave propagation via network approximations\, with low communication and computational cost; (ii) A direct\, nonlinear acoustic imaging algorithm in strongly heterogeneous media\, where the ROM is used to manipulate the data in such a way that multiply scattered waves are separated from the single scattered ones. \nDr. Druskin is being hosted by Prof. Borcea (Mathematics) and Prof. Schotland (Mathematics & Physics). If you would like to meet him\, please send an email to micde-contact@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-vladimir-druskin-schlumberger-doll-research/
LOCATION:1360 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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