See also:

• Computers: Artificial Intelligence: People  (213)
• Science: Social Sciences: Psychology: Cognitive: People  (156)

Adelson, Edward T. - - Visual perception, machine vision, image processing.

Agakov, Felix - - Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.

Allan, Moray - - Computer vision, probabilistic models for image sequences, invariant features.

Amari, Shun-ichi - - Neural network learning, information geometry.

Andonie, Razvan - - Data structures for computational intelligence.

Andrieu, Christophe - - Particle filtering and Monte Carlo Markov Chain methods.

Anthony, Martin - - Computational learning theory, discrete mathematics.

Attias, Hagai - - Graphical models, variational Bayes, independent factor analysis.

Bach, Francis - - Machine learning, kernel methods, kernel independent component analysis and graphical model.

Ballard, Dana H. - - Visual perception with neural networks.

Bartlett, Marian Stewart - - Image analysis with unsupervised learning, face recognition, facial expression analysis.

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.

Bengio, Samy - - Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification.

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

Bishop, Chris - - Graphical models, variational methods, pattern recognition.

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

Brody, Carlos D. - - Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.

Brown, Andrew - - Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.

Bulsari, A. - - Neural networks and nonlinear modelling for process engineering.

Calvin, William H. - - Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.

Caruana, Rich - - Multitask learning.

Cheung, Vincent - - Machine learning and probabilistic graphical models for computer vision and computational molecular biology.

Chu, Selina - - Artificial intelligence, machine learning, data mining.

Coolen, Ton - - Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.

Cottrell, Garrison W. - - An artrificial intelligence researcher who is an expert on neural networks.

Dahlem, Markus A. - - Neural network models of visual cortex to model neurological symptoms of migraine.

Dayan , Peter - - Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.

de Freitas, Nando - - Bayesian inference, Markov chain Monte Carlo simulation, machine learning.

de Garis, Hugo - - Evolvable neural network models, neural networks for programmable hardware, large neural networks.

de Sa, Virginia - - Supervised and unsupervised learning, cross-modal learning.

De vito, Saverio - - Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architecture.

De Wilde, Philippe - - Brain inspired models of uncertainty, linguistic and fuzzy uncertainty, uncertainty in dynamic multi-user environments.

Dietterich, Thomas G. - - Reinforcement learning, machine learning, supervised learning.

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.

Freeman, William T. - - Bayesian perception, computer vision, image processing.

Frey, Brendan J. - - Iterative decoding, unsupervised learning, graphical models.

Friedman, Nir - - Learning of probabilistic models, applications to computational biology.

Frohlich, Jochen - - Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.

Fujita, Hajime - - Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems.

Garcia, Christophe - - Computer vision, image analysis, neural networks.

Ghahramani, Zoubin - - Sensorimotor control, unsupervised learning, probabilistic machine learning.

Hansen, Lars Kai - - Neural network ensembles, adaptive systems and applications in neuroinformatics.

Herbrich, Ralph - - Statistical learning theory, support vector machines and kernel methods.

Heskes, Tom - - Learning and generalization in neural networks.

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

Honavar, Vasant - - Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.

Hopfield, John J. - - Neural networks, collective behaviour of systems of simple processors. Most noted for Hopfield networks.

Hughes, Nicholas - - Automated Analysis of ECG.

Jaakkola, Tommi S. - - Graphical models, variational methods, kernel methods.

Jensen, Finn Verner - - Graphical models, belief propagation.

Jordan, Michael I. - - Graphical models, variational methods, machine learning, reasoning under uncertainty.

Joseph Wakeling's Neural Systems Research Page - - Research papers and information on biologically inspired neural networks, brain modelling, AI and related topics. A cross-disciplinary site mixing information from physics, neuroscience, cognitive science and other fields.

Joshi, Prashant - - Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.

Kakade, Sham - - Reinforcement learning and conditioning, mathematical models of neural processing.

Kali, Szabolcs - - Learning and memory in the brain, hippocampus.

Kappen, Bert - - Boltzmann machines, computational neurobiology, online learning.

Kearns, Michael - - Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.

Keysers, Daniel - - Pattern recognition and statistical modelling for object recognition.

Koller, Daphne - - Probabilistic models for complex uncertain domains.

Lafferty, John D. - - Statistical machine learning, text and natural language processing, information retrieval, information theory.

Lawrence, Neil - - Probabilistic models, variational methods.

Lawrence, Steve - - Information dissemination and retrieval, machine learning and neural networks.

LeCun, Yann - - Handwritten recognition, convolutional networks, image compression. Noted for LeNet.

Leen, Todd - - Online learning, machine learning, learning dynamics.

Leow, Wee Kheng - - Computer vision, computational olfaction.

Lerner, Uri N. - - Hybrid and Bayesian networks.

Li, Zhaoping - - Non-linear neural dynamics, visual segmentation, sensory processing.

Maass, Wolfgang - - Theory of computation, computation in spiking neurons.

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

McCallum, Andrew - - Machine learning, text and information retrieval and extraction, reinforcement learning.

Meila, Marina - - Graphical models, learning in high dimensions, tree networks.

Mika, Sebastian - - Machine learning and explorative data analysis: support vector machines, kernel principal component analysis and kernel Fisher discriminant analysis.

Minka, Thomas P. - - Machine learning, computer vision, Bayesian methods.

Morris, Quaid - - Machine learning for medical diagnosis and biological data analysis.

Muresan, Raul C. - - Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.

Murphy, Kevin P. - - Graphical models, machine learning, reinforcement learning.

Murray, Alan - - Neural networks and VLSI hardware.

Murray-Smith, Roderick - - Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.

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

Ng, Andrew - - Reinforcement learning, machine learning.

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

Olier, Ivan - - Artificial intelligence, generative topographic map, missing data.

Olshausen, Bruno - - Visual coding, statistics of images, independent components analysis.

Opper, Manfred - - Statistical physics, information theory and applied probability and applications to machine learning and complex systems.

Paccanaro, Alberto - - Learning distributed representation of concepts from relational data.

Pathegama, Mahinda - - Intelligent information systems, physiological sciences systems.

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

Phillips, Jonathon - - Face recognition.

Rao, Rajesh P. N. - - Models of human and computer vision.

Rasmussen, Carl Edward - - Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.

Revow, Michael - - Hand-written character recognition.

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

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

Roweis, Sam T. - - Speech processing, auditory scene analysis, machine learning.

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

Rutkowski, Leszek - - Neural networks, fuzzy systems, computational intelligence.

Saad, David - - Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.

Sahani, Maneesh - - Statistical analysis of neural data, experimental design in neuroscience.

Sallans, Brian - - Decision making under uncertainty, reinforcement learning, unsupervised learning.

Saul, Lawrence K. - - Machine learning, pattern recognition, neural networks, voice processing, auditory computation.

Saund, Eric - - Intermediate level structure in vision.

Schein, Andrew I. - - Machine learning approaches to data mining focussing on text mining applications.

Schetinin, Vitaly - - Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques.

Sejnowski, Terry - - Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.

Seung, Sebastian - - Short-term memory, learning and memory in the brain, computational learning theory.

Shkolnik, Alexander - - Neurally controlled robotics.

Shuurmans, Dale - - Computational learning, complex probability modelling.

Simard, Patrice - - Machine learning and generalization.

Smola, Alex J. - - Kernel methods for prediction and data analysis.

Storkey, Amos - - Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.

Sutton, Richard S. - - Reinforcement learning.

Sykacek, Peter - - Brain Computer Interface.

Teh, Yee Whye - - Learning and inference in complex probabilistic models.

Tipping, Mike - - Bayesian learning, relevance vector machine, probabilistic principal component analysis.

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

Versace, Massimiliano - - Neural networks applied to visual perception and computational modeling of mental disorders.

Wainwright, Martin - - Statistical signal and image processing, natural image modelling, graphical models.

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

Weiss, Yair - - Vision, Bayesian methods, neural computation.

Welling, Max - - Unsupervised learning, probabilistic density estimation, machine vision.

Wiegerinck, Wim - - Inference in graphical models, mean field and variational approaches.

Williams, Christopher K. I. - - Gaussian processes, image interpretation, graphical models, pattern recognition.

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

Wiskott, Laurenz - - Face recognition, Invariances in learning and vision.

Wu, Yingnian - - Stochastic generative models for complex visual phenomena.

Wunsch II, Donald C. - - Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection.

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

Yedidia, Jonathan S. - - Statistical methods for inference and learning.

Zemel, Richard - - Unsupervised learning, machine learning, computational models of neural processing.

Zhou, Zhi-Hua - - Neural computing, data mining, evolutionary computing, ensemble networks.