People Neural Networks Artificial Intelligence

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• Chu, Selina - Artificial intelligence, machine learning, data mining.
  
• Olshausen, Bruno - Visual coding, statistics of images, independent components analysis.
  
• Heskes, Tom - Learning and generalization in neural networks.
  
• Zhou, Zhi-Hua - Neural computing, data mining, evolutionary computing, ensemble networks.
  
• Cheung, Vincent - Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
  
• De Wilde, Philippe - Brain inspired models of uncertainty, linguistic and fuzzy uncertainty, uncertainty in dynamic multi-user environments.
  
• Ghahramani, Zoubin - Sensorimotor control, unsupervised learning, probabilistic machine learning.
  
• Beveridge, Ross - Computer vision, model-based object recognition, face recognition.
  
• Williams, Christopher K. I. - Gaussian processes, image interpretation, graphical models, pattern recognition.
  
• Malchiodi, Dario - Machine learning, Learning from uncertain data.
  
• Adelson, Edward T. - Visual perception, machine vision, image processing.
  
• de Freitas, Nando - Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
  
• Zemel, Richard - Unsupervised learning, machine learning, computational models of neural processing.
  
• Meila, Marina - Graphical models, learning in high dimensions, tree networks.
  
• Olier, Ivan - Artificial intelligence, generative topographic map, missing data.
  
• Rutkowski, Leszek - Neural networks, fuzzy systems, computational intelligence.
  
• Freeman, William T. - Bayesian perception, computer vision, image processing.
  
• Wiskott, Laurenz - Face recognition, Invariances in learning and vision.
  
• Dietterich, Thomas G. - Reinforcement learning, machine learning, supervised learning.
  
• 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.
  
• Herbrich, Ralph - Statistical learning theory, support vector machines and kernel methods.
  
• Saul, Lawrence K. - Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
  
• Bach, Francis - Machine learning, kernel methods, kernel independent component analysis and graphical models
  
• Frey, Brendan J. - Iterative decoding, unsupervised learning, graphical models.
  
• Rao, Rajesh P. N. - Models of human and computer vision.
  
• Welling, Max - Unsupervised learning, probabilistic density estimation, machine vision.
  
• Wallis, Guy - Object recognition, cognitive neuroscience, interaction between vision and motor movements.
  
• Murray-Smith, Roderick - Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
  
• 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.
  
• Hughes, Nicholas - Automated Analysis of ECG.
  
• Sykacek, Peter - Brain Computer Interface.
  
• Calvin, William H. - Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
  
• Wainwright, Martin - Statistical signal and image processing, natural image modelling, graphical models.
  
• MacKay, David - Bayesian theory and inference, error-correcting codes, machine learning.
  
• Li, Zhaoping - Non-linear neural dynamics, visual segmentation, sensory processing.
  
• Shuurmans, Dale - Computational learning, complex probability modelling.
  
• Pearlmutter, Barak - Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
  
• Leow, Wee Kheng - Computer vision, computational olfaction.
  
• Lawrence, Neil - Probabilistic models, variational methods.
  
• Lafferty, John D. - Statistical machine learning, text and natural language processing, information retrieval, information theory.
  
• Maass, Wolfgang - Theory of computation, computation in spiking neurons.
  
• Bartlett, Marian Stewart - Image analysis with unsupervised learning, face recognition, facial expression analysis.
  
• Ballard, Dana H. - Visual perception with neural networks.
  
• Andrieu, Christophe - Particle filtering and Monte Carlo Markov Chain methods.
  
• Paccanaro, Alberto - Learning distributed representation of concepts from relational data.
  
• Winther, Ole - Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
  
• Bishop, Chris - Graphical models, variational methods, pattern recognition.
  
• Joshi, Prashant - Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
  
• Frohlich, Jochen - Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
  
• Weiss, Yair - Vision, Bayesian methods, neural computation.
  
• Caruana, Rich - Multitask learning.
  
• Hinton, Geoffrey E. - Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
  
• Schein, Andrew I. - Machine learning approaches to data mining focussing on text mining applications.
  
• Beal, Matthew J. - Bayesian inference, variational methods, graphical models, nonparametric Bayes.
  
• Neal, Radford - Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
  
• Sallans, Brian - Decision making under uncertainty, reinforcement learning, unsupervised learning.
  
• Saad, David - Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.
  
• Murphy, Kevin P. - Graphical models, machine learning, reinforcement learning.
  
• Sejnowski, Terry - Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
  
• Anthony, Martin - Computational learning theory, discrete mathematics.
  
• Shkolnik, Alexander - Neurally controlled robotics.
  
• Coolen, Ton - Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
  
• McCallum, Andrew - Machine learning, text and information retrieval and extraction, reinforcement learning.
  
• Sahani, Maneesh - Statistical analysis of neural data, experimental design in neuroscience.
  
• Hansen, Lars Kai - Neural network ensembles, adaptive systems and applications in neuroinformatics.
  
• Cottrell, Garrison W. - An artrificial intelligence researcher who is an expert on neural networks.
  
• Andonie, Razvan - Data structures for computational intelligence.
  
• Allan, Moray - Computer vision, probabilistic models for image sequences, invariant features.
  
• 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.
  
• Brown, Andrew - Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
  
• Honavar, Vasant - Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
  
• Murray, Alan - Neural networks and VLSI hardware.
  
• Simard, Patrice - Machine learning and generalization.
  
• Minka, Thomas P. - Machine learning, computer vision, Bayesian methods.
  
• Friedman, Nir - Learning of probabilistic models, applications to computational biology.
  
• Dahlem, Markus A. - Neural network models of visual cortex to model neurological symptoms of migraine.
  
• LeCun, Yann - Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
  
• Seung, Sebastian - Short-term memory, learning and memory in the brain, computational learning theory.
  
• Rasmussen, Carl Edward - Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
  
• Garcia, Christophe - Computer vision, image analysis, neural networks.
  
• Boutilier, Craig - Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
  
• De vito, Saverio - Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
  
• Muresan, Raul C. - Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
  
• Leen, Todd - Online learning, machine learning, learning dynamics.
  
• Becker, Sue - Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
  
• Wu, Yingnian - Stochastic generative models for complex visual phenomena.
  
• Jordan, Michael I. - Graphical models, variational methods, machine learning, reasoning under uncertainty.
  
• Koller, Daphne - Probabilistic models for complex uncertain domains.
  
• Saund, Eric - Intermediate level structure in vision.
  
• Teh, Yee Whye - Learning and inference in complex probabilistic models.
  
• Tipping, Mike - Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods.
  
• Revow, Michael - Hand-written character recognition.
  
• Lerner, Uri N. - Hybrid and Bayesian networks.
  
• Brody, Carlos D. - Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.
  
• Lawrence, Steve - Information dissemination and retrieval, machine learning and neural networks.
  
• Opper, Manfred - Statistical physics, information theory and applied probability and applications to machine learning and complex systems.
  
• Yedidia, Jonathan S. - Statistical methods for inference and learning.
  
• Russell, Stuart - Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
  
• Attias, Hagai - Graphical models, variational Bayes, independent factor analysis.
  
• Roberts, Stephen - Machine learning and medical data analysis, independent component analysis and information theory.
  
• Amari, Shun-ichi - Neural network learning, information geometry.
  
• de Garis, Hugo - Evolvable neural network models, neural networks for programmable hardware, large neural networks.
  
• Kearns, Michael - Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
  
• Xing, Eric - Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
  
• Bulsari, A. - Neural networks and nonlinear modelling for process engineering.
  
• Sutton, Richard S. - Reinforcement learning.
  
• Jensen, Finn Verner - Graphical models, belief propagation.
  
• Dayan , Peter - Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
  
• Jaakkola, Tommi S. - Graphical models, variational methods, kernel methods.
  
• Oja, Erkki - Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
  
• Rovetta, Stefano - Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
  
  

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