Bamdev Mishra

[+] Research statement

  • Low-rank factorizations for large-scale optimization algorithms:

    The research deals with the application of manifold optimization techniques to large-scale convex optimization problems whose expected or desired solutions have low rank. We focus specifically on convex relaxations of large-scale rank-constrained problems encountered in machine learning, data reduction and bioinformatics.

    As of now, I am trying to understand this comic strip.


[+] Education and research experience

[+] Publications and codes



[+] Teaching and graduate courses

  • 2012 Sem 1: SYST003 Lab (Teaching Assistant)
  • 2011 Sem 2: Numerical Optimization (Teaching Assistant): Tutorials
  • 2011 Sem 1: Theory of Computation
  • INRIA/ENS: 2011 July: CVML Summer school
  • EECI: 2011 Feb: Optimization on Matrix Manifolds
  • SOCN: 2010 Dec: Compressed Sensing and Sparse Approximations
  • Sys017: 2010 Sem 1: Nonlinear Systems


[+] Activities

  • Actively involved in the open-source MATLAB toolbox Manopt for optimization on matrix manifold
  • Teaching assistant at ULg
  • Currently maintaining the research unit website systmod


[+] Recent updates

  • startag Manopt toolbox is officially released...

    Manopt comes with a large library of manifolds and ready-to-use Riemannian optimization algorithms. It is well documented and includes diagnostics tools to help you get started quickly and make sure you make no mistakes along the way. It is designed to provide great flexibility in describing your cost function and incorporates an optional caching system for more efficiency.

  • startag qGeomMC: A Quotient Geometric approach to low-rank Matrix Completion

    This package contains a MATLAB implementation of algorithms for the low-rank matrix completion problem. The present version includes gradient descent and conjugate gradient algorithms based on the fixed-rank geometry proposed in the techreport [arXiv:1211.1550].

  • startag Updated TraceNorm code to handle large scale matrices
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