Topics for graduate research

  • Brain-Computer interface
  • Motion classification and anticipation from EEG and EMG signals for virtual reality applications and for technological aids for the neuro-motor impaired
  • Function estimation by Support Vector Machines
  • Random embedding and boosting machines for pattern recognition
  • Clustering and unsupervised learning by Bayesian methods.
  • Support vector machines for clustering and pattern recognition.
  • Augmented (real+virtual) reality for aiding people with movement disorders. (there is a special fellowship for this).
  • Information geometric methods for sampling, learning and optimization.
  • Collision-free traffic control by neural networks.
  • Process prediction (e.g., financial forcasting) by nonlinear methods and neural networks.

Please contact Prof. Baram for details.