Computers

Computer vision, computational olfaction.

Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.

Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.

Graphical models, variational methods, machine learning, reasoning under uncertainty.

Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.

Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.

Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.

Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning, causal inference, knowledge representation, preference reasoning.

Computer vision, model-based object recognition, face recognition.

Unsupervised learning, machine learning, computational models of neural processing.

Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.

Graphical models, learning in high dimensions, tree networks.

Multitask learning.

Stochastic generative models for complex visual phenomena.

Models of human and computer vision.

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

Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.

Bayesian inference, Markov chain Monte Carlo simulation, machine learning.

Handwritten recognition, convolutional networks, image compression. Noted for LeNet.

Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.

Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.

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

Machine learning and medical data analysis, independent component analysis and information theory.

Graphical models, variational methods, pattern recognition.

Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.

Bayesian theory and inference, error-correcting codes, machine learning.

Vision, Bayesian methods, neural computation.

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

Learning of probabilistic models, applications to computational biology.

Reinforcement learning, machine learning, supervised learning.

Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.

Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.

Automated Analysis of ECG.

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

Neural networks and nonlinear modelling for process engineering.

Computational learning theory, discrete mathematics.

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

Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.

Learning and generalization in neural networks.

Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures

Hybrid and Bayesian networks.

Probabilistic models for complex uncertain domains.

Machine learning, text and information retrieval and extraction, reinforcement learning.

Statistical learning, machine learning approaches to computational biology, pattern recognition and control.

Bayesian inference, variational methods, graphical models, nonparametric Bayes.

Neural networks, fuzzy systems, computational intelligence.

Machine learning, computer vision, Bayesian methods.

Learning distributed representation of concepts from relational data.

Neural network ensembles, adaptive systems and applications in neuroinformatics.

Bayesian perception, computer vision, image processing.