Mili Shah
Associate Professor
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Department of Mathematics and Statistics
Loyola University Maryland
4501 N. Charles Street
Baltimore, MD 21210-2699
mishah@loyola.edu
Knott Hall 316d
410.617.2724

research
I am working with the National Institute of Standards and Technology on calibration and registration problems which have applications in computer vision, manufacturing, and robotics. For more information, check my CVIU paper.

I am also working on calculating a symmetry preserving singular value decomposition (SPSVD), which is a matrix factorization that gives the best symmetric low rank approximation to a set of data. This decomposition has applications in molecular dynamics and face detection. For more information, check my SIMAX paper.

teaching fall 2014
  • Programming in Mathematics
  • Applied Calculus

  • past courses
  • Numerical Analysis
  • Real Analysis
  • Differential Equations
  • Calculus
  • Precalculus

  • papers
    [10] M. Shah, Solving the Robot-World/Hand-Eye Calibration Problem Using the Kronecker Product, ASME Journal of Mechanisms and Robotics, Volume 5, Issue 3 (2013).

    [9] M. Shah, R. D. Eastman, T. Hong, An Overview of Robot-Sensor Calibration Methods for Evaluation of Perception Systems, Performance Metrics for Intelligent Systems, (2012).

    [8] M. Shah, Comparing Two Sets of Corresponding Six Degree of Freedom Data, Computer Vision and Image Understanding, Volume 115, Issue 10 (2011), pp. 1355-1362.

    [7] T. Chang, T. Hong, J. Falco, M. Shneier, R. Eastman, M. Shah, Methodology for Evaluating Static Six-Degree-of-Freedom (6DoF) Perception Systems, Performance Metrics for Intelligent Systems, (2010).

    [6] M. I. Shah, Symmetric Eigenfaces, Technical Report, Loyola University Maryland (2009), TR2009_01.

    [5] M. Shah, T. Chang, T. Hong, R. Eastman, Mathematical Metrology for Evaluating a 6DOF Visual Servoing System, Performance Metrics for Intelligent Systems, (2009).

    [4] M. I. Shah and D. C. Sorensen, Best Non-Spherical Symmetric Low Rank Approximation, SIAM Journal on Matrix Analysis and Applications, 31 (2009), pp. 1019-1039.

    [3] M. I. Shah and D. C. Sorensen, A Symmetry Preserving Singular Value Decomposition, SIAM Journal on Matrix Analysis and Applications, 28 (2006), pp. 749-769.

    [2] W. Wriggers, Z. Zhang, M. Shah, and D. C. Sorensen, Simulating Nanoscale Functional Motions of Biomolecules, Molecular Simulation, 32 (2006), pp. 803-815.

    [1] D. C. Sorensen and M. Shah, Principal Component Analysis and Model Reduction for Dynamical Systems with Symmetry Constraints, European Control Conference (CDC-ECC), 2005, pp. 2260-2264.