Minsky, Marvin@ (11) 1. Adelson, Edward T. - Visual perception, machine vision, image processing. 2. Allan, Moray - Computer vision, probabilistic models for image sequences, invariant features. 3. Andonie, Razvan - Data structures for computational intelligence. 4. Beal, Matthew J. - Bayesian inference, variational methods, graphical models, nonparametric Bayes. 5. Becker, Sue - Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems. 6. Bulsari, A. - Neural networks and nonlinear modelling for process engineering. 7. Calvin, William H. - Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think. 8. Heskes, Tom - Learning and generalization in neural networks. 9. Jordan, Michael I. - Graphical models, variational methods, machine learning, reasoning under uncertainty. 10. Roweis, Sam T. - Speech processing, auditory scene analysis, machine learning. 11. Rutkowski, Leszek - Neural networks, fuzzy systems, computational intelligence. 12. Wiskott, Laurenz - Face recognition, Invariances in learning and vision. 13. Agakov, Felix - Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality. 14. Amari, Shun-ichi - Neural network learning, information geometry. 15. Andrieu, Christophe - Particle filtering and Monte Carlo Markov Chain methods. 16. Anthony, Martin - Computational learning theory, discrete mathematics. 17. Attias, Hagai - Graphical models, variational Bayes, independent factor analysis. 18. Bach, Francis - Machine learning, kernel methods, kernel independent component analysis and graphical models 19. Ballard, Dana H. - Visual perception with neural networks. 20. Bartlett, Marian Stewart - Image analysis with unsupervised learning, face recognition, facial expression analysis. 21. Bengio, Samy - Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification. 22. Beveridge, Ross - Computer vision, model-based object recognition, face recognition. 23. Bishop, Chris - Graphical models, variational methods, pattern recognition. 24. Boutilier, Craig - Decision making and planning under uncertainty, reinforcement learning, game theory and economic models. 25. Brody, Carlos D. - Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology. 26. Caruana, Rich - Multitask learning. 27. Cheung, Vincent - Machine learning and probabilistic graphical models for computer vision and computational molecular biology. 28. Chu, Selina - Artificial intelligence, machine learning, data mining. 29. Coolen, Ton - Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks. 30. Cottrell, Garrison W. - An artrificial intelligence researcher who is an expert on neural networks. 31. Dahlem, Markus A. - Neural network models of visual cortex to model neurological symptoms of migraine. 32. de Freitas, Nando - Bayesian inference, Markov chain Monte Carlo simulation, machine learning. 33. de Garis, Hugo NEW! - Evolvable neural network models, neural networks for programmable hardware, large neural networks. 34. De vito, Saverio - Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures 35. De Wilde, Philippe - Brain inspired models of uncertainty, linguistic and fuzzy uncertainty, uncertainty in dynamic multi-user environments. 36. Dietterich, Thomas G. - Reinforcement learning, machine learning, supervised learning. 37. 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. 38. Freeman, William T. - Bayesian perception, computer vision, image processing. 39. Frey, Brendan J. - Iterative decoding, unsupervised learning, graphical models. 40. Friedman, Nir - Learning of probabilistic models, applications to computational biology. 41. Frohlich, Jochen - Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps. 42. Fujita, Hajime - Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems. 43. Garcia, Christophe - Computer vision, image analysis, neural networks. 44. Hansen, Lars Kai - Neural network ensembles, adaptive systems and applications in neuroinformatics. 45. Herbrich, Ralph - Statistical learning theory, support vector machines and kernel methods. 46. Hinton, Geoffrey E. - Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation. 47. Honavar, Vasant - Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning. 48. Jaakkola, Tommi S. - Graphical models, variational methods, kernel methods. 49. Jensen, Finn Verner - Graphical models, belief propagation. 50. 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. 51. Joshi, Prashant - Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons. 52. Kearns, Michael - Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems. 53. Keysers, Daniel - Pattern recognition and statistical modelling for object recognition. 54. Koller, Daphne - Probabilistic models for complex uncertain domains. 55. Lafferty, John D. - Statistical machine learning, text and natural language processing, information retrieval, information theory. 56. Lawrence, Neil - Probabilistic models, variational methods. 57. Lawrence, Steve - Information dissemination and retrieval, machine learning and neural networks. 58. LeCun, Yann - Handwritten recognition, convolutional networks, image compression. Noted for LeNet. 59. Leen, Todd - Online learning, machine learning, learning dynamics. 60. Leow, Wee Kheng - Computer vision, computational olfaction. 61. Lerner, Uri N. - Hybrid and Bayesian networks. 62. MacKay, David - Bayesian theory and inference, error-correcting codes, machine learning. 63. McCallum, Andrew - Machine learning, text and information retrieval and extraction, reinforcement learning. 64. Meila, Marina - Graphical models, learning in high dimensions, tree networks. 65. Minka, Thomas P. - Machine learning, computer vision, Bayesian methods. 66. Muresan, Raul C. - Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures. 67. Murphy, Kevin P. - Graphical models, machine learning, reinforcement learning. 68. Murray, Alan - Neural networks and VLSI hardware. 69. Murray-Smith, Roderick - Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces. 70. Neal, Radford - Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression. 71. Oja, Erkki - Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis. 72. Olshausen, Bruno - Visual coding, statistics of images, independent components analysis. 73. Opper, Manfred - Statistical physics, information theory and applied probability and applications to machine learning and complex systems. 74. Paccanaro, Alberto - Learning distributed representation of concepts from relational data. 75. Pearlmutter, Barak - Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging. 76. Rao, Rajesh P. N. - Models of human and computer vision. 77. Revow, Michael - Hand-written character recognition. 78. Rovetta, Stefano - Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities. 79. Russell, Stuart - Many aspects of probabilistic modelling, identity uncertainty, expressive probability models. 80. Saad, David - Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques. 81. Saul, Lawrence K. - Machine learning, pattern recognition, neural networks, voice processing, auditory computation. 82. Saund, Eric - Intermediate level structure in vision. 83. Schein, Andrew I. - Machine learning approaches to data mining focussing on text mining applications. 84. Schetinin, Vitaly - Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques. 85. Sejnowski, Terry - Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations. 86. Seung, Sebastian - Short-term memory, learning and memory in the brain, computational learning theory. 87. Shkolnik, Alexander - Neurally controlled robotics. 88. Shuurmans, Dale - Computational learning, complex probability modelling. 89. Simard, Patrice - Machine learning and generalization. 90. Smola, Alex J. - Kernel methods for prediction and data analysis. 91. Storkey, Amos - Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks. 92. Sutton, Richard S. - Reinforcement learning. 93. Teh, Yee Whye - Learning and inference in complex probabilistic models. 94. Tipping, Mike - Bayesian learning, relevance vector machine, probabilistic principal component analysis. 95. Tishby, Naftali - Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science. 96. Versace, Massimiliano - Neural networks applied to visual perception and computational modeling of mental disorders. 97. Wainwright, Martin - Statistical signal and image processing, natural image modelling, graphical models. 98. Wallis, Guy - Object recognition, cognitive neuroscience, interaction between vision and motor movements. 99. Weiss, Yair - Vision, Bayesian methods, neural computation. 100. Welling, Max - Unsupervised learning, probabilistic density estimation, machine vision. 101. Wiegerinck, Wim - Inference in graphical models, mean field and variational approaches. 102. Williams, Christopher K. I. - Gaussian processes, image interpretation, graphical models, pattern recognition. 103. Winther, Ole - Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction. 104. Wu, Yingnian - Stochastic generative models for complex visual phenomena. 105. Wunsch II, Donald C. - Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection. 106. Xing, Eric - Statistical learning, machine learning approaches to computational biology, pattern recognition and control. 107. Yedidia, Jonathan S. - Statistical methods for inference and learning. 108. Zemel, Richard - Unsupervised learning, machine learning, computational models of neural processing. 109. Zhou, Zhi-Hua - Neural computing, data mining, evolutionary computing, ensemble networks.

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