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