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.

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

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

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

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

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

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

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

Ballard, Dana H.
Visual perception with neural networks.

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

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

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

Jensen, Finn Verner
Graphical models, belief propagation.

Murray, Alan
Neural networks and VLSI hardware.

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

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

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.

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

Revow, Michael
Hand-written character recognition.

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

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

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

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

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

Sutton, Richard S.
Reinforcement learning.

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

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

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

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

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

Shuurmans, Dale
Computational learning, complex probability modelling.

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

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

Saund, Eric
Intermediate level structure in vision.

Ng, Andrew
Reinforcement learning, machine learning.

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.

Pathegama, Mahinda
Intelligent information systems, physiological sciences systems.

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

Caruana, Rich
Multitask learning.

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

Phillips, Jonathon
Face recognition.

Simard, Patrice
Machine learning and generalization.

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

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

Zhu, Song Chun
Vision and graphics, statistical modelling and computing, neural computation.

Wu, Yingnian
Stochastic generative models for complex visual phenomena.

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

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

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

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

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

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

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

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

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

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

Dovzhenko, Alexander Yu.
Neural networks for computer clusters, oscillations in neural networks

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

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

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

Koller, Daphne
Probabilistic models for complex uncertain domains.

Lerner, Uri N.
Hybrid and Bayesian networks.

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.

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

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

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

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

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

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

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

Bach, Francis
Machine learning, kernel methods, kernel independent component analysis and graphical models

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

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

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

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

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

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.

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

Weiss, Yair
Vision, Bayesian methods, neural computation.

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

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.

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

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

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

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

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

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

Lawrence, Neil
Probabilistic models, variational methods.

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

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

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

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

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

Sykacek, Peter
Brain Computer Interface.

Hughes, Nicholas
Automated Analysis of ECG.

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

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

Beal, Matthew J.
Bayesian inference, variational methods, graphical models.

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

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

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

Anthony, Martin
Computational learning theory, discrete mathematics.

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

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

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

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

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

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

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

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

Kawato, Mitsuo
Computational neuroscience, neural network modelling.

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

Andonie, Razvan
Data structures for computational intelligence.

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

Shkolnik, Alexander
Neurally controlled robotics.

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

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

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

Heskes, Tom
Learning and generalization in neural networks.