CSE Seminar: Kamran Paynabar

Event Details
  • Date/Time:
    • Friday November 8, 2013 - Saturday November 9, 2013
      1:00 pm - 1:59 pm
  • Location: Klaus 1447
  • Phone:
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Le Song~~~ lsong@cc.gatech.edu


Summary Sentence: Change Detection and Diagnosis in Time-Series of Multivariate Functional Data

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Speaker: Kamran Paynabar, Associate Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology


Change Detection and Diagnosis in Time-Series of Multivariate Functional Data


Change detection in time-series of multivariate (multiple) functional data remains an important and challenging problem in statistical process control. Motivated by real applications in multi-operation forging processes, in this talk, we present a new semi-parametric method for monitoring, change-detection and fault diagnosis in multivariate functional data. The proposed approach integrates multi-dimensional functional principal component analysis (MFPCA) with change-point modeling. In this approach, multivariate profiles are treated as multivariate functional observations and their low-dimensional projections on the principal components of data are used to develop a change-point model. A BIC-based diagnosis procedure is also proposed for fault diagnosis and identification of altered functional data streams.  We validate our method using several simulations and a case study of process monitoring in multi-operation forging processes. We also discuss the theoretical properties of the proposed change-detection model and change-point estimator.


Kamran Paynabar is Assistant Professor in H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his B.Sc. and M.Sc. in Industrial Engineering from Iran University of Science and Technology and Azad University in 2002 and 2004, respectively, and his Ph.D. in Industrial and Operations Engineering from The University of Michigan in 2012. He also holds an M.A. in Statistics from The University of Michigan. His research interests include data fusion for multi-stream waveform signals and functional data, engineering-driven statistical modeling, sensor selection in distributed sensing networks, probabilistic graphical models, and statistical learning with applications in manufacturing and healthcare systems. He is the recipient of the INFORMS Data Mining Best Student Paper Award, the Best Application Paper Award from IIE Transactions, and the Wilson Prize for the Best Student Paper in Manufacturing.


Additional Information

In Campus Calendar

High Performance Computing (HPC), Computational Science and Engineering, College of Computing, School of Computer Science, School of Interactive Computing, School of Computational Science and Engineering

Invited Audience
Undergraduate students, Faculty/Staff, Graduate students
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  • Created By: Lometa Mitchell
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  • Created On: Nov 6, 2013 - 4:02am
  • Last Updated: Apr 13, 2017 - 5:23pm