research
I run miliLab - a lab focused on designing, building, and evaluating different robotic and vision based systems which include exoskeletons, robotic arms, and motion tracking systems. This work is done in collaboration with the National Institute of Standards and Technology and involves calibration and registration problems which have applications in many fields including manufacturing and health care.

My background is in numerical linear algebra and therefore love matrix factorization problems. For my PhD, I developed a symmetry preserving singular value decomposition, 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.

Selected publications are listed below. A full list of my publications can be found on Google Scholar.

selected publications

• M. Shah, R. Bostelman, S. Legowik, T. Hong, Calibration of Mobile Manipulators using 2D Positional Features, Measurement, Volume 124 (2018), pp. 322-328.
• 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).
• 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).
• 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.
• 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.