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.