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» Adelson, Edward T. - Visual perception, machine vision, image processing.
» Agakov, Felix - Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.
» Amari, Shun-ichi - Neural network learning, information geometry.
» Andrieu, Christophe - Particle filtering and Monte Carlo Markov Chain methods.
» Anthony, Martin - Computational learning theory, discrete mathematics.
» Appl, Martin - Reinforcement learning, soft-computing, optimization.
» 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.
» 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.
» 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.
» Dayan , Peter - Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
» Dietterich, Thomas G. - Reinforcement learning, machine learning, supervised learning.
» Dovzhenko, Alexander Yu. - Neural networks for computer clusters, oscillations in neural networks
» 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.
» 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.
» Kawato, Mitsuo - Computational neuroscience, neural network modelling.
» 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.
» Olshausen, Bruno - Visual coding, statistics of images, independent components analysis.
» Opper, Manfred - Statistical physics, information theory and applied probability and applications to machien 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 seperation 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.
» 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.
» 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.
» Zhu, Song Chun - Vision and graphics, statistical modelling and computing, neural computation.
» 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.