- Becker, Sue - http://www.science.mcmaster.ca/Psychology/sb.html
Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
- Jordan, Michael I. - http://www.cs.berkeley.edu/~jordan/
Graphical models, variational methods, machine learning, reasoning under uncertainty.
- Sejnowski, Terry - http://www.salk.edu/faculty/sejnowski.html
Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
- Maass, Wolfgang - http://www.cis.tu-graz.ac.at/igi/maass/
Theory of computation, computation in spiking neurons.
- Neal, Radford - http://www.cs.toronto.edu/~radford
Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
- Adelson, Edward T. - http://www-bcs.mit.edu/people/adelson/
Visual perception, machine vision, image processing.
- Brody, Carlos D. - http://www.cns.caltech.edu/~carlos/
Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.
- Dayan , Peter - http://www.gatsby.ucl.ac.uk/~dayan/
Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
- Ballard, Dana H. - http://www.cs.rochester.edu/users/faculty/dana
Visual perception with neural networks.
- Freeman, William T. - http://www.merl.com/people/freeman
Bayesian perception, computer vision, image processing.
- Ghahramani, Zoubin - http://www.gatsby.ucl.ac.uk/~zoubin
Sensorimotor control, unsupervised learning, probabilistic machine learning.
- Jaakkola, Tommi S. - http://www.ai.mit.edu/people/tommi
Graphical models, variational methods, kernel methods.
- Murray, Alan - http://www.ee.ed.ac.uk/~afm/
Neural networks and VLSI hardware.
- Oja, Erkki - http://www.cis.hut.fi/oja/
Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
- Leen, Todd - http://www.cse.ogi.edu/~tleen
Online learning, machine learning, learning dynamics.
- Leow, Wee Kheng - http://www.comp.nus.edu.sg/~leowwk
Computer vision, computational olfaction.
- Li, Zhaoping - http://www.gatsby.ucl.ac.uk/~zhaoping
Non-linear neural dynamics, visual segmentation, sensory processing.
- Murphy, Kevin P. - http://www.cs.berkeley.edu/~murphyk
Graphical models, machine learning, reinforcement learning.
- Schetinin, Vitaly - http://nnlab.tripod.com
Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques.
- Revow, Michael - http://www.cs.toronto.edu/~revow/
Hand-written character recognition.
- Sahani, Maneesh - http://www.gatsby.ucl.ac.uk/~maneesh/
Statistical analysis of neural data, experimental design in neuroscience.
- Seung, Sebastian - http://hebb.mit.edu/people/seung/
Short-term memory, learning and memory in the brain, computational learning theory.
- Bartlett, Marian Stewart - http://ergo.ucsd.edu/~marni/
Image analysis with unsupervised learning, face recognition, facial expression analysis.
- Calvin, William H. - http://faculty.washington.edu/wcalvin/
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
- Honavar, Vasant - http://www.cs.iastate.edu/~honavar/
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
- Sutton, Richard S. - http://www-anw.cs.umass.edu/~rich/sutton.html
Reinforcement learning.
- Beveridge, Ross - http://www.cs.colostate.edu/~ross/
Computer vision, model-based object recognition, face recognition.
- de Sa, Virginia - http://keck.ucsf.edu/~desa/
Supervised and unsupervised learning, cross-modal learning.
- Saad, David - http://www.ncrg.aston.ac.uk/People/saadd
Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.
- Teh, Yee Whye - http://www.cs.utoronto.ca/~ywteh
Learning and inference in complex probabilistic models.
- Shuurmans, Dale - http://www.lpaig.uwaterloo.ca:80/~dale/
Computational learning, complex probability modelling.
- Olshausen, Bruno - http://redwood.ucdavis.edu/bruno/
Visual coding, statistics of images, independent components analysis.
- Lafferty, John D. - http://www.cs.cmu.edu/afs/cs.cmu.edu/user/lafferty/www/homepage.html
Statistical machine learning, text and natural language processing, information retrieval, information theory.
- Ng, Andrew - http://www.cs.berkeley.edu/~ang/
Reinforcement learning, machine learning.
- Zemel, Richard - http://www.cs.utoronto.ca/~zemel/
Unsupervised learning, machine learning, computational models of neural processing.
- Boutilier, Craig - http://www.cs.toronto.edu/~cebly/
Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
- Pathegama, Mahinda - http://www.kes.unisa.edu.au/~mahinda/index.htm
Intelligent information systems, physiological sciences systems.
- Meila, Marina - http://www.stat.washington.edu/mmp/
Graphical models, learning in high dimensions, tree networks.
- Caruana, Rich - http://www.cs.cmu.edu/~caruana/
Multitask learning.
- Wiskott, Laurenz - http://itb.biologie.hu-berlin.de/~wiskott/homepage.html
Face recognition, Invariances in learning and vision.
- Phillips, Jonathon - http://www.itl.nist.gov/iaui/894.03/staff/jonathon.html
Face recognition.
- Simard, Patrice - http://www.research.microsoft.com/~patrice/
Machine learning and generalization.
- Opper, Manfred - http://www.ncrg.aston.ac.uk/People/opperm/
Statistical physics, information theory and applied probability and applications to machien learning and complex systems.
- Yedidia, Jonathan S. - http://www.merl.com/people/yedidia/
Statistical methods for inference and learning.
- Wu, Yingnian - http://www.stat.ucla.edu/~ywu/
Stochastic generative models for complex visual phenomena.
- Rasmussen, Carl Edward - http://www.gatsby.ucl.ac.uk/~edward
Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
- Sallans, Brian - http://www.gatsby.ucl.ac.uk/~sallans
Decision making under uncertainty, reinforcement learning, unsupervised learning.
- Brown, Andrew - http://www.gatsby.ucl.ac.uk/~andy
Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
- Paccanaro, Alberto - http://www.gatsby.ucl.ac.uk/~alberto/
Learning distributed representation of concepts from relational data.
- Morris, Quaid - http://www.gatsby.ucl.ac.uk/~quaid
Machine learning for medical diagnosis and biological data analysis.
- Kakade, Sham - http://www.gatsby.ucl.ac.uk/~sham
Reinforcement learning and conditioning, mathematical models of neural processing.
- Kali, Szabolcs - http://www.gatsby.ucl.ac.uk/~szabolcs
Learning and memory in the brain, hippocampus.
- Welling, Max - http://www.cs.utoronto.ca/~welling
Unsupervised learning, probabilistic density estimation, machine vision.
- Wallis, Guy - http://www.uq.edu.au/~uqgwalli/
Object recognition, cognitive neuroscience, interaction between vision and motor movements.
- Wunsch II, Donald C. - http://www.ece.umr.edu/~dwunsch/
Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection.
- Keysers, Daniel - http://www-i6.Informatik.RWTH-Aachen.DE/~keysers/
Pattern recognition and statistical modelling for object recognition.
- Rao, Rajesh P. N. - http://www.cs.washington.edu/homes/rao/
Models of human and computer vision.
- Tishby, Naftali - http://www.cs.huji.ac.il/~tishby/
Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
- Rovetta, Stefano - http://www.disi.unige.it/person/RovettaS/
Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
- de Freitas, Nando - http://www.cs.ubc.ca/~nando/
Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
- Saul, Lawrence K. - http://www.cis.upenn.edu/~lsaul/
Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
- LeCun, Yann - http://yann.lecun.com/
Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
- Kearns, Michael - http://www.cis.upenn.edu/~mkearns/
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
- Storkey, Amos - http://www.anc.ed.ac.uk/~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. - http://www.cs.toronto.edu/~roweis/
Speech processing, auditory scene analysis, machine learning.
- Coolen, Ton - http://www.mth.kcl.ac.uk/~tcoolen/
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
- Minka, Thomas P. - http://www.stat.cmu.edu/~minka/
Machine learning, computer vision, Bayesian methods.
- Bach, Francis - http://www.cs.berkeley.edu/~fbach/
Machine learning, kernel methods, kernel independent component analysis and graphical models
- Winther, Ole - http://eivind.imm.dtu.dk/staff/winther/
Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
- Herbrich, Ralph - http://www.research.microsoft.com/users/rherb/
Statistical learning theory, support vector machines and kernel methods.
- Roberts, Stephen - http://www.robots.ox.ac.uk/~sjrob/
Machine learning and medical data analysis, independent component analysis and information theory.
- Bishop, Chris - http://research.microsoft.com/~cmbishop/
Graphical models, variational methods, pattern recognition.
- Cottrell, Garrison W. - http://charlotte.ucsd.edu/~gary/
An artrificial intelligence researcher who is an expert on neural networks.
- Frey, Brendan J. - http://www.psi.utoronto.ca/~frey/
Iterative decoding, unsupervised learning, graphical models.
- Hinton, Geoffrey E. - http://www.cs.toronto.edu/~hinton/
Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
- MacKay, David - http://www.inference.phy.cam.ac.uk/mackay/
Bayesian theory and inference, error-correcting codes, machine learning.
- Smola, Alex J. - http://mlg.anu.edu.au/~smola/
Kernel methods for prediction and data analysis.
- Weiss, Yair - http://www.cs.huji.ac.il/~yweiss/
Vision, Bayesian methods, neural computation.
- Williams, Christopher K. I. - http://www.dai.ed.ac.uk/homes/ckiw/
Gaussian processes, image interpretation, graphical models, pattern recognition.
- Joseph Wakeling's Neural Systems Research Page - http://neuro.webdrake.net/
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 - http://www.cs.usu.edu/~degaris/
Evolvable neural network models, neural networks for programmable hardware, large neural networks.
- Friedman, Nir - http://www.cs.huji.ac.il/~nir/
Learning of probabilistic models, applications to computational biology.
- Tipping, Mike - http://research.microsoft.com/users/mtipping/pages/mtipping.htm
Bayesian learning, relevance vector machine, probabilistic principal component analysis.
- Bengio, Samy - http://www.idiap.ch/~bengio/index_en.html
Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification.
- Dietterich, Thomas G. - http://cs.oregonstate.edu/~tgd/
Reinforcement learning, machine learning, supervised learning.
- Lawrence, Neil - http://www.dcs.shef.ac.uk/~neil
Probabilistic models, variational methods.
- Hopfield, John J. - http://neuron.princeton.edu/~john/
Neural networks, collective behaviour of systems of simple processors. Most noted for Hopfield networks.
- Russell, Stuart - http://www.cs.berkeley.edu/~russell/
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
- Mika, Sebastian - http://ida.first.gmd.de/homepages/mika/
Machine learning and explorative data analysis: support vector machines, kernel principal component analysis and kernel Fisher discriminant analysis.
- Murray-Smith, Roderick - http://www.dcs.gla.ac.uk/~rod/
Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
- Sykacek, Peter - http://www.robots.ox.ac.uk/~psyk/
Brain Computer Interface.
- Zhou, Zhi-Hua - http://cs.nju.edu.cn/people/zhouzh/
Neural computing, data mining, evolutionary computing, ensemble networks.
- Wainwright, Martin - http://www.eecs.berkeley.edu/~martinw/
Statistical signal and image processing, natural image modelling, graphical models.
- Beal, Matthew J. - http://www.cs.toronto.edu/~beal
Bayesian inference, variational methods, graphical models.
- Bulsari, A. - http://www.abo.fi/~abulsari
Neural networks and nonlinear modelling for process engineering.
- Agakov, Felix - http://www.inf.ed.ac.uk/people/staff/Felix_Agakov.html
Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.
- Andrieu, Christophe - http://www.stats.bris.ac.uk/~maxca/
Particle filtering and Monte Carlo Markov Chain methods.
- Anthony, Martin - http://www.maths.lse.ac.uk/Personal/martin/
Computational learning theory, discrete mathematics.
- Garcia, Christophe - http://www.csd.uoc.gr/~cgarcia
Computer vision, image analysis, neural networks.
- Versace, Massimiliano - http://www.maxversace.com
Neural networks applied to visual perception and computational modeling of mental disorders.
- Joshi, Prashant - http://www.igi.tugraz.at/joshi
Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
- Pearlmutter, Barak - http://www-bcl.cs.may.ie/~barak/
Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
- Fujita, Hajime - http://hawaii.aist-nara.ac.jp/~hajime-f/
Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems.
- Chu, Selina - http://www-scf.usc.edu/~selinach
Artificial intelligence, machine learning, data mining.
- Schein, Andrew I. - http://www.cis.upenn.edu/~ais
Machine learning approaches to data mining focussing on text mining applications.
- Frohlich, Jochen - http://rfhs8012.fh-regensburg.de/~saj39122/jfroehl/diplom/e-index.html
Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
- Andonie, Razvan - http://www.cwu.edu/~andonie/
Data structures for computational intelligence.
- Allan, Moray - http://www.morayallan.com/
Computer vision, probabilistic models for image sequences, invariant features.
- Wiegerinck, Wim - http://www.mbfys.ru.nl/mbfys/people/wimw/
Inference in graphical models, mean field and variational approaches.
- Kappen, Bert - http://www.mbfys.ru.nl/~bert/
Boltzmann machines, computational neurobiology, online learning.
- Heskes, Tom - http://www.cs.ru.nl/~tomh/
Learning and generalization in neural networks.
- De vito, Saverio - http://www.afs.enea.it/devito/
Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
- Olier, Ivan - http://www.lsi.upc.edu/%7eiaolier/
Artificial intelligence, generative topographic map, missing data.
- Lerner, Uri N. - http://ai.stanford.edu/~uri/
Hybrid and Bayesian networks.
- Koller, Daphne - http://ai.stanford.edu/~koller/
Probabilistic models for complex uncertain domains.
- Dr Hooman Shadnia - http://ca.geocities.com/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.
- Saund, Eric - http://www2.parc.com/spl/members/saund/
Intermediate level structure in vision.
- Cheung, Vincent - http://www.psi.toronto.edu/~vincent/
Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
- McCallum, Andrew - http://www.cs.umass.edu/~mccallum/
Machine learning, text and information retrieval and extraction, reinforcement learning.
- Xing, Eric - http://www.cs.cmu.edu/~epxing/
Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
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