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Teaching

Inverse problems in signal and image processing, computer vision

  • Super resolution
  • Image restoration
  • Optical flow and motion estimation
  • Image priors, Bayesian approach
  • Sparse decomposition of signals
  • Shape from moments
  • Nonlinear filtering of signals
  • Stochastic estimators
  • Image compression
  • Video processing

Numerical analysis - optimization theory, Numerical Linear Algebra

  • Large linear systems
  • Linear programming and primal-dual methods
  • Non-linear programming
  • Multi-grid
  • Dynamic systems (Kalman Filter)
  • Eigenvalue problems
  • L1 and L0 minimization

Machine learning algorithms

  • Support Vector Machine (SVM) and related methods
  • Application - Detection of faces in images
  • Searching in multimedia databases
  • Dimensionality reduction