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The University of Maryland Institute for Advanced Computer Studies (UMIACS) is a research unit within the College of Computer, Mathematical, and Physical Sciences whose mission is to foster interdisciplinary research and education in computing. The Institute's research programs are led by distinguished faculty most of whom hold joint appointments in the departments of Computer Science, Electrical and Computer Engineering, Geography, Linguistics, Philosophy, the College of Education, Robert H. Smith School of Business, College of Life Sciences, and College of Information Studies. Major sponsored research programs address fundamental issues at the interface between Computer Science and other disciplines, and are supported by an advanced computing infrastructure.

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Recent Submissions

  • Body Maps on Human Chromosomes 

    Cherniak, Christopher; Rodriguez-Esteban, Raul (2015-11-08)
    An exploration of the hypothesis that human genes are organized somatotopically: For each autosomal chromosome, its tissue-specific genes tend to have relative positions on the chromosome that mirror corresponding positions ...
  • Accurate computation of Galerkin double surface integrals in the 3-D boundary element method 

    Adelman, Ross; Gumerov, Nail A.; Duraiswami, Ramani (2015-05-29)
    Many boundary element integral equation kernels are based on the Green’s functions of the Laplace and Helmholtz equations in three dimensions. These include, for example, the Laplace, Helmholtz, elasticity, Stokes, and ...
  • A Stochastic Approach to Uncertainty in the Equations of MHD Kinematics 

    Phillips, Edward G.; Elman, Howard C. (2014-07-10)
    The magnetohydodynamic (MHD) kinematics model describes the electromagnetic behavior of an electrically conducting fluid when its hydrodynamic properties are assumed to be known. In particular, the MHD kinematics equations ...
  • Preconditioning Techniques for Reduced Basis Methods for Parameterized Partial Differential Equations 

    Elman, Howard C.; Forstall, Virginia (2014-05-27)
    The reduced basis methodology is an efficient approach to solve parameterized discrete partial differential equations when the solution is needed at many parameter values. An offline step approximates the solution space ...
  • Anomaly Detection for Symbolic Representations 

    Cox, Michael T.; Paisner, Matt; Oates, Tim; Perlis, Don (2014-03-25)
    A fully autonomous agent recognizes new problems, explains what causes such problems, and generates its own goals to solve these problems. Our approach to this goal-driven model of autonomy uses a methodology called the ...

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