Artificial Intelligence: Neural Networks: People: Page 11

Bayesian learning, relevance vector machine, probabilistic principal component analysis.

Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.

Neural networks applied to visual perception and computational modeling of mental disorders.

Statistical signal and image processing, natural image modelling, graphical models.

Object recognition, cognitive neuroscience, interaction between vision and motor movements.

Vision, Bayesian methods, neural computation.

Unsupervised learning, probabilistic density estimation, machine vision.

Inference in graphical models, mean field and variational approaches.

Gaussian processes, image interpretation, graphical models, pattern recognition.

Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.