| 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 models |
| 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 vito, Saverio: Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| Pearlmutter, Barak: Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging. |
| 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. |
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