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Adelson, Edward T. - http://web.mit.edu/persci/people/adelson/
Visual perception, machine vision, image processing. |
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Russell, Stuart - http://www.cs.berkeley.edu/~russell/
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models. |
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MacKay, David - http://www.inference.phy.cam.ac.uk/mackay/
Bayesian theory and inference, error-correcting codes, machine learning. |
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Bishop, Chris - http://research.microsoft.com/~cmbishop/
Graphical models, variational methods, pattern recognition. |
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Jordan, Michael I. - http://www.cs.berkeley.edu/~jordan/
Graphical models, variational methods, machine learning, reasoning under uncertainty. |
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Koller, Daphne - http://ai.stanford.edu/~koller/
Probabilistic models for complex uncertain domains. |
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Brown, Andrew - http://www.ecs.soton.ac.uk/people/adb
Machine learning of dynamic data, graphical models and Bayesian networks, neural networks. |
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Murphy, Kevin P. - http://www.cs.berkeley.edu/~murphyk
Graphical models, machine learning, reinforcement learning. |
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Freeman, William T. - http://people.csail.mit.edu/billf/wtf.html
Bayesian perception, computer vision, image processing. |
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Shkolnik, Alexander - http://web.mit.edu/shkolnik/www/
Neurally controlled robotics. |
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LeCun, Yann - http://yann.lecun.com/
Handwritten recognition, convolutional networks, image compression. Noted for LeNet. |
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Roweis, Sam T. - http://www.cs.toronto.edu/~roweis/
Speech processing, auditory scene analysis, machine learning. |
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Lawrence, Steve - http://labs.google.com/people/lawrence/
Information dissemination and retrieval, machine learning and neural networks. |
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Weiss, Yair - http://www.cs.huji.ac.il/~yweiss/
Vision, Bayesian methods, neural computation. |
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Seung, Sebastian - http://hebb.mit.edu/people/seung/
Short-term memory, learning and memory in the brain, computational learning theory. |
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McCallum, Andrew - http://www.cs.umass.edu/~mccallum/
Machine learning, text and information retrieval and extraction, reinforcement learning. |
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Williams, Christopher K. I. - http://www.dai.ed.ac.uk/homes/ckiw/
Gaussian processes, image interpretation, graphical models, pattern recognition. |
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Jaakkola, Tommi S. - http://www.ai.mit.edu/people/tommi
Graphical models, variational methods, kernel methods. |
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Kearns, Michael - http://www.cis.upenn.edu/~mkearns/
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems. |
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Wiskott, Laurenz - http://itb.biologie.hu-berlin.de/~wiskott/homepage.html
Face recognition, Invariances in learning and vision. |
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Minka, Thomas P. - http://research.microsoft.com/~minka/
Machine learning, computer vision, Bayesian methods. |
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Bach, Francis - http://www.di.ens.fr/~fbach/
Machine learning, kernel methods, kernel independent component analysis and graphical models |
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Lawrence, Neil - http://www.dcs.shef.ac.uk/~neil
Probabilistic models, variational methods. |
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Wainwright, Martin - http://www.eecs.berkeley.edu/~martinw/
Statistical signal and image processing, natural image modelling, graphical models. |
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de Freitas, Nando - http://www.cs.ubc.ca/~nando/
Bayesian inference, Markov chain Monte Carlo simulation, machine learning. |
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Friedman, Nir - http://www.cs.huji.ac.il/~nir/
Learning of probabilistic models, applications to computational biology. |
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Murray-Smith, Roderick - http://www.dcs.gla.ac.uk/~rod/
Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces. |
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Ghahramani, Zoubin - http://www.gatsby.ucl.ac.uk/~zoubin
Sensorimotor control, unsupervised learning, probabilistic machine learning. |
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Dayan , Peter - http://www.gatsby.ucl.ac.uk/~dayan/
Representation and learning in neural processing systems, unsupervised learning, reinforcement learning. |
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Yedidia, Jonathan S. - http://www.merl.com/people/yedidia/
Statistical methods for inference and learning. |
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Rasmussen, Carl Edward - http://www.gatsby.ucl.ac.uk/~edward
Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models. |
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Calvin, William H. - http://faculty.washington.edu/wcalvin/
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think. |
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Herbrich, Ralph - http://www.research.microsoft.com/users/rherb/
Statistical learning theory, support vector machines and kernel methods. |
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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. |
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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. |
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Frey, Brendan J. - http://www.psi.utoronto.ca/~frey/
Iterative decoding, unsupervised learning, graphical models. |
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Dietterich, Thomas G. - http://cs.oregonstate.edu/~tgd/
Reinforcement learning, machine learning, supervised learning. |
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Tishby, Naftali - http://www.cs.huji.ac.il/~tishby/
Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science. |
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Rao, Rajesh P. N. - http://www.cs.washington.edu/homes/rao/
Models of human and computer vision. |
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Roberts, Stephen - http://www.robots.ox.ac.uk/~sjrob/
Machine learning and medical data analysis, independent component analysis and information theory. |
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Lerner, Uri N. - http://ai.stanford.edu/~uri/
Hybrid and Bayesian networks. |
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Tipping, Mike - http://www.miketipping.com
Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods. |
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Cottrell, Garrison W. - http://charlotte.ucsd.edu/~gary/
An artrificial intelligence researcher who is an expert on neural networks. |
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Olshausen, Bruno - http://redwood.berkeley.edu/bruno
Visual coding, statistics of images, independent components analysis. |
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Kakade, Sham - http://www.gatsby.ucl.ac.uk/~sham
Reinforcement learning and conditioning, mathematical models of neural processing. |
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Saul, Lawrence K. - http://www.cs.ucsd.edu/~saul/
Machine learning, pattern recognition, neural networks, voice processing, auditory computation. |
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Maass, Wolfgang - http://www.igi.tugraz.at/maass/
Theory of computation, computation in spiking neurons. |
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Wu, Yingnian - http://www.stat.ucla.edu/~ywu/
Stochastic generative models for complex visual phenomena. |
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Jensen, Finn Verner - http://www.cs.auc.dk/~fvj
Graphical models, belief propagation. |
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Simard, Patrice - http://www.research.microsoft.com/~patrice/
Machine learning and generalization. |
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Prashant, Joshi - http://www.klab.caltech.edu/~joshi/
Computational neuroscientist, with main areas of research interest being computational motor control, computational models of olfaction, computation with spiking neurons, neurocomputational basis of working memory and decision making, learning in biologically realistic circuits. |
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Li, Zhaoping - http://www.gatsby.ucl.ac.uk/~zhaoping
Non-linear neural dynamics, visual segmentation, sensory processing. |
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Sejnowski, Terry - http://www.salk.edu/faculty/faculty_details.php?id=48
Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations. |
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Zhou, Zhi-Hua - http://cs.nju.edu.cn/zhouzh/
Neural computing, data mining, evolutionary computing, ensemble networks. |
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Sutton, Richard S. - http://www-anw.cs.umass.edu/~rich/sutton.html
Reinforcement learning. |
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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. |
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Beveridge, Ross - http://www.cs.colostate.edu/~ross/
Computer vision, model-based object recognition, face recognition. |
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De Wilde, Philippe - http://www.macs.hw.ac.uk/~pdw/
Brain inspired models of uncertainty, linguistic and fuzzy uncertainty, uncertainty in dynamic multi-user environments. |
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Hansen, Lars Kai - http://eivind.imm.dtu.dk/staff/lkhansen/lkhansen.html
Neural network ensembles, adaptive systems and applications in neuroinformatics. |
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Beal, Matthew J. - http://www.cse.buffalo.edu/faculty/mbeal
Bayesian inference, variational methods, graphical models, nonparametric Bayes. |
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Smola, Alex J. - http://mlg.anu.edu.au/~smola/
Kernel methods for prediction and data analysis. |
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Sahani, Maneesh - http://www.gatsby.ucl.ac.uk/~maneesh/
Statistical analysis of neural data, experimental design in neuroscience. |
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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. |
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Bartlett, Marian Stewart - http://ergo.ucsd.edu/~marni/
Image analysis with unsupervised learning, face recognition, facial expression analysis. |
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Hughes, Nicholas - http://www.robots.ox.ac.uk/~nph/
Automated Analysis of ECG. |
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de Garis, Hugo - http://www.iss.whu.edu.cn/degaris/
Evolvable neural network models, neural networks for programmable hardware, large neural networks. |
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Saad, David - http://www.ncrg.aston.ac.uk/People/saadd/Welcome.html
Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques. |
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Allan, Moray - http://www.morayallan.com/
Computer vision, probabilistic models for image sequences, invariant features. |
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Andrieu, Christophe - http://www.stats.bris.ac.uk/~maxca/
Particle filtering and Monte Carlo Markov Chain methods. |
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Heskes, Tom - http://www.cs.ru.nl/~tomh/
Learning and generalization in neural networks. |
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Agakov, Felix - http://www.inf.ed.ac.uk/people/staff/Felix_Agakov.html
Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality. |
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Anthony, Martin - http://www.maths.lse.ac.uk/Personal/martin/
Computational learning theory, discrete mathematics. |
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Becker, Sue - http://www.science.mcmaster.ca/Psychology/sb.html
Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems. |
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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. |
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Coolen, Ton - http://www.mth.kcl.ac.uk/~tcoolen/
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks. |
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Schein, Andrew I. - http://www.cis.upenn.edu/~ais
Machine learning approaches to data mining focussing on text mining applications. |
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Leen, Todd - http://www.cse.ogi.edu/~tleen
Online learning, machine learning, learning dynamics. |
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Leow, Wee Kheng - http://www.comp.nus.edu.sg/~leowwk
Computer vision, computational olfaction. |
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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. |
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Opper, Manfred - http://www.ncrg.aston.ac.uk/People/opperm/Welcome.html
Statistical physics, information theory and applied probability and applications to machine learning and complex systems. |
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Cheung, Vincent - http://www.psi.toronto.edu/~vincent/
Machine learning and probabilistic graphical models for computer vision and computational molecular biology. |
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Saund, Eric - http://www2.parc.com/spl/members/saund/
Intermediate level structure in vision. |
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Malchiodi, Dario - http://homes.dsi.unimi.it/~malchiod/
Machine learning, Learning from uncertain data. |
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Kali, Szabolcs - http://www.gatsby.ucl.ac.uk/~szabolcs
Learning and memory in the brain, hippocampus. |
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Wiegerinck, Wim - http://www.mbfys.ru.nl/mbfys/people/wimw/
Inference in graphical models, mean field and variational approaches. |
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Sallans, Brian - http://members.chello.at/hoebertz-sallans/sallans/index.html
Decision making under uncertainty, reinforcement learning, unsupervised learning. |
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Wallis, Guy - http://www.uq.edu.au/~uqgwalli/
Object recognition, cognitive neuroscience, interaction between vision and motor movements. |
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Olier, Ivan - http://www.lsi.upc.edu/~iaolier/
Artificial intelligence, generative topographic map, missing data. |
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Chu, Selina - http://www-scf.usc.edu/~selinach
Artificial intelligence, machine learning, data mining. |
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Paccanaro, Alberto - http://homes.gersteinlab.org/people/alberto/
Learning distributed representation of concepts from relational data. |
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Attias, Hagai - http://research.goldenmetallic.com/
Graphical models, variational Bayes, independent factor analysis. |
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Muresan, Raul C. - http://www.raulmuresan.home.ro/
Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures. |
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De vito, Saverio - http://www.afs.enea.it/devito/
Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures |
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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. |
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Rutkowski, Leszek - http://www.kik.pcz.czest.pl/~rutkowski/
Neural networks, fuzzy systems, computational intelligence. |
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Neal, Radford - http://www.cs.toronto.edu/~radford
Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression. |
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Caruana, Rich - http://www.cs.cmu.edu/~caruana/
Multitask learning. |
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Meila, Marina - http://www.stat.washington.edu/mmp/
Graphical models, learning in high dimensions, tree networks. |
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Ballard, Dana H. - http://www.cs.rochester.edu/users/faculty/dana
Visual perception with neural networks. |
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Welling, Max - http://www.cs.utoronto.ca/~welling
Unsupervised learning, probabilistic density estimation, machine vision. |
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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. |
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Boutilier, Craig - http://www.cs.toronto.edu/~cebly/
Decision making and planning under uncertainty, reinforcement learning, game theory and economic models. |
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Zemel, Richard - http://www.cs.utoronto.ca/~zemel/
Unsupervised learning, machine learning, computational models of neural processing. |
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Brody, Carlos D. - http://www.cshl.edu/public/SCIENCE/brody.html
Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology. |
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Lafferty, John D. - http://www.cs.cmu.edu/~lafferty/
Statistical machine learning, text and natural language processing, information retrieval, information theory. |
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Dahlem, Markus A. - http://www.migraine-aura.org/EN/Markus_Dahlem.html
Neural network models of visual cortex to model neurological symptoms of migraine. |
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Teh, Yee Whye - http://www.cs.utoronto.ca/~ywteh
Learning and inference in complex probabilistic models. |
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Amari, Shun-ichi - http://www.brain.riken.jp/labs/mns/amari/home-E.html
Neural network learning, information geometry. |
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Oja, Erkki - http://www.cis.hut.fi/oja/
Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis. |
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Xing, Eric - http://www.cs.cmu.edu/~epxing/
Statistical learning, machine learning approaches to computational biology, pattern recognition and control. |
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Honavar, Vasant - http://www.cs.iastate.edu/~honavar/
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning. |
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Sykacek, Peter - http://www.robots.ox.ac.uk/~psyk/
Brain Computer Interface. |
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Revow, Michael - http://www.cs.toronto.edu/~revow/
Hand-written character recognition. |
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Bulsari, A. - http://www.abo.fi/~abulsari
Neural networks and nonlinear modelling for process engineering. |
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Pearlmutter, Barak - http://www-bcl.cs.may.ie/~barak/
Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging. |