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.