Williams, Christopher K. I. - Gaussian processes, image interpretation, graphical models, pattern recognition. Meila, Marina - Graphical models, learning in high dimensions, tree networks. Roberts, Stephen - Machine learning and medical data analysis, independent component analysis and information theory. Beal, Matthew J. - Bayesian inference, variational methods, graphical models, nonparametric Bayes. Becker, Sue - Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems. Jordan, Michael I. - Graphical models, variational methods, machine learning, reasoning under uncertainty. Neal, Radford - Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression. Dayan , Peter - Representation and learning in neural processing systems, unsupervised learning, reinforcement learning. Jaakkola, Tommi S. - Graphical models, variational methods, kernel methods. Oja, Erkki - Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis. Leow, Wee Kheng - Computer vision, computational olfaction. Li, Zhaoping - Non-linear neural dynamics, visual segmentation, sensory processing. Murphy, Kevin P. - Graphical models, machine learning, reinforcement learning. Calvin, William H. - Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think. Beveridge, Ross - Computer vision, model-based object recognition, face recognition. Zemel, Richard - Unsupervised learning, machine learning, computational models of neural processing. Boutilier, Craig - Decision making and planning under uncertainty, reinforcement learning, game theory and economic models. Caruana, Rich - Multitask learning. Wu, Yingnian - Stochastic generative models for complex visual phenomena. Wallis, Guy - Object recognition, cognitive neuroscience, interaction between vision and motor movements. Rao, Rajesh P. N. - Models of human and computer vision. Tishby, Naftali - Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science. Rovetta, Stefano - Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities. de Freitas, Nando - Bayesian inference, Markov chain Monte Carlo simulation, machine learning. LeCun, Yann - Handwritten recognition, convolutional networks, image compression. Noted for LeNet. Kearns, Michael - Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems. Coolen, Ton - Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks. Winther, Ole - Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction. Bishop, Chris - Graphical models, variational methods, pattern recognition. Frey, Brendan J. - Iterative decoding, unsupervised learning, graphical models. Hinton, Geoffrey E. - Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation. MacKay, David - Bayesian theory and inference, error-correcting codes, machine learning. Weiss, Yair - Vision, Bayesian methods, neural computation. Friedman, Nir - Learning of probabilistic models, applications to computational biology. Dietterich, Thomas G. - Reinforcement learning, machine learning, supervised learning. Russell, Stuart - Many aspects of probabilistic modelling, identity uncertainty, expressive probability models. Murray-Smith, Roderick - Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces. Hughes, Nicholas - Automated Analysis of ECG. Wainwright, Martin - Statistical signal and image processing, natural image modelling, graphical models. Bulsari, A. - Neural networks and nonlinear modelling for process engineering. Andrieu, Christophe - Particle filtering and Monte Carlo Markov Chain methods. Anthony, Martin - Computational learning theory, discrete mathematics. Versace, Massimiliano - Neural networks applied to visual perception and computational modeling of mental disorders. Pearlmutter, Barak - Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging. Allan, Moray - Computer vision, probabilistic models for image sequences, invariant features. Heskes, Tom - Learning and generalization in neural networks. De vito, Saverio - Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures Lerner, Uri N. - Hybrid and Bayesian networks. Koller, Daphne - Probabilistic models for complex uncertain domains. McCallum, Andrew - Machine learning, text and information retrieval and extraction, reinforcement learning. Xing, Eric - Statistical learning, machine learning approaches to computational biology, pattern recognition and control. Muresan, Raul C. - Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures. Amari, Shun-ichi - Neural network learning, information geometry. Rutkowski, Leszek - Neural networks, fuzzy systems, computational intelligence. Minka, Thomas P. - Machine learning, computer vision, Bayesian methods. Lafferty, John D. - Statistical machine learning, text and natural language processing, information retrieval, information theory. Paccanaro, Alberto - Learning distributed representation of concepts from relational data. Hansen, Lars Kai - Neural network ensembles, adaptive systems and applications in neuroinformatics. Freeman, William T. - Bayesian perception, computer vision, image processing. Maass, Wolfgang - Theory of computation, computation in spiking neurons. Brown, Andrew - Machine learning of dynamic data, graphical models and Bayesian networks, neural networks. Olshausen, Bruno - Visual coding, statistics of images, independent components analysis. Saul, Lawrence K. - Machine learning, pattern recognition, neural networks, voice processing, auditory computation. Tipping, Mike - Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods. Zhou, Zhi-Hua - Neural computing, data mining, evolutionary computing, ensemble networks. Bach, Francis - Machine learning, kernel methods, kernel independent component analysis and graphical models Malchiodi, Dario - Machine learning, Learning from uncertain data. Rasmussen, Carl Edward - Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models. Storkey, Amos - Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks. Dr Hooman Shadnia - Dedicated to artificial neural networks and their applications in medical research and computational chemistry. Offers a quick tutorial on theory on ANNs written in Persian. Prashant, Joshi - Computational Neuroscientist. Research interests: reservoir computing, computational motor control, computation with spiking neurons. Peterson, Leif E. - Researcher at Methodist Hospital Research Institute on classification technology and related fields. Xiaoguang, Rui - Researcher at University of Science and Technology of China. About image annotation, image retrieval, social network analysis, pattern recognition and machine learning. Grangier, David - Research focusing on Machine Learning, Neural Networks, Kernel Machines, Computer Vision and Speech Processing. Roweis, Sam T. - Speech processing, auditory scene analysis, machine learning. Adelson, Edward T. - Visual perception, machine vision, image processing. Honavar, Vasant - Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning, causal inference, knowledge representation, preference reasoning.