Directory of People Resources

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Subcategories

• Minsky, Marvin

Resources in This Category

• Adelson, Edward T.

  http://persci.mit.edu/people/adelson
  Visual perception, machine vision, image processing.

• Allan, Moray

  http://www.morayallan.com/
  Computer vision, probabilistic models for image sequences, invariant features.

• Amari, Shun-ichi

  http://www.brain.riken.jp/labs/mns/amari/home-E.html
  Neural network learning, information geometry.

• 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.

• Bach, Francis

  http://www.di.ens.fr/~fbach/
  Machine learning, kernel methods, kernel independent component analysis and graphical models

• Beal, Matthew J.

  http://www.cse.buffalo.edu/faculty/mbeal
  Bayesian inference, variational methods, graphical models, nonparametric Bayes.

• Becker, Sue

  http://www.science.mcmaster.ca/Psychology/sb.html
  Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.

• Beveridge, Ross

  http://www.cs.colostate.edu/~ross/
  Computer vision, model-based object recognition, face recognition.

• Bishop, Chris

  http://research.microsoft.com/~cmbishop/
  Graphical models, variational methods, pattern recognition.

• Boutilier, Craig

  http://www.cs.toronto.edu/~cebly/
  Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.

• Brown, Andrew

  http://www.ecs.soton.ac.uk/people/adb
  Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.

• Bulsari, A.

  http://www.abo.fi/~abulsari
  Neural networks and nonlinear modelling for process engineering.

• Calvin, William H.

  http://faculty.washington.edu/wcalvin/
  Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.

• Caruana, Rich

  http://www.cs.cmu.edu/~caruana/
  Multitask learning.

• Coolen, Ton

  http://www.mth.kcl.ac.uk/~tcoolen/
  Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.

• Dayan , Peter

  http://www.gatsby.ucl.ac.uk/~dayan/
  Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.

• de Freitas, Nando

  http://www.cs.ubc.ca/~nando/
  Bayesian inference, Markov chain Monte Carlo simulation, machine learning.

• De vito, Saverio

  http://www.afs.enea.it/devito/
  Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures

• Dietterich, Thomas G.

  http://cs.oregonstate.edu/~tgd/
  Reinforcement learning, machine learning, supervised learning.

• Dr Hooman Shadnia

  http://www.shadnia.com/
  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.

• Freeman, William T.

  http://people.csail.mit.edu/billf/wtf.html
  Bayesian perception, computer vision, image processing.

• Frey, Brendan J.

  http://www.psi.utoronto.ca/~frey/
  Iterative decoding, unsupervised learning, graphical models.

• Friedman, Nir

  http://www.cs.huji.ac.il/~nir/
  Learning of probabilistic models, applications to computational biology.

• Grangier, David

  http://david.grangier.info/
  Research focusing on Machine Learning, Neural Networks, Kernel Machines, Computer Vision and Speech Processing.

• Hansen, Lars Kai

  http://eivind.imm.dtu.dk/staff/lkhansen/lkhansen.html
  Neural network ensembles, adaptive systems and applications in neuroinformatics.

• Heskes, Tom

  http://www.cs.ru.nl/~tomh/
  Learning and generalization in neural networks.

• 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.

• Honavar, Vasant

  http://www.ist.psu.edu/directory/faculty/vuh14
  Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning, causal inference, knowledge representation, preference reasoning.

• Hughes, Nicholas

  http://www.robots.ox.ac.uk/~nph/
  Automated Analysis of ECG.

• Jaakkola, Tommi S.

  http://www.ai.mit.edu/people/tommi
  Graphical models, variational methods, kernel methods.

• Jordan, Michael I.

  http://www.cs.berkeley.edu/~jordan/
  Graphical models, variational methods, machine learning, reasoning under uncertainty.

• Kearns, Michael

  http://www.cis.upenn.edu/~mkearns/
  Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.

• Koller, Daphne

  http://ai.stanford.edu/~koller/
  Probabilistic models for complex uncertain domains.

• Lafferty, John D.

  http://www.cs.cmu.edu/~lafferty/
  Statistical machine learning, text and natural language processing, information retrieval, information theory.

• LeCun, Yann

  http://yann.lecun.com/
  Handwritten recognition, convolutional networks, image compression. Noted for LeNet.

• Leow, Wee Kheng

  http://www.comp.nus.edu.sg/~leowwk
  Computer vision, computational olfaction.

• Lerner, Uri N.

  http://ai.stanford.edu/~uri/
  Hybrid and Bayesian networks.

• Li, Zhaoping

  http://www.gatsby.ucl.ac.uk/~zhaoping
  Non-linear neural dynamics, visual segmentation, sensory processing.

• Maass, Wolfgang

  http://www.igi.tugraz.at/maass/
  Theory of computation, computation in spiking neurons.

• MacKay, David

  http://www.inference.phy.cam.ac.uk/mackay/
  Bayesian theory and inference, error-correcting codes, machine learning.

• Malchiodi, Dario

  http://homes.dsi.unimi.it/~malchiod/
  Machine learning, Learning from uncertain data.

• McCallum, Andrew

  http://www.cs.umass.edu/~mccallum/
  Machine learning, text and information retrieval and extraction, reinforcement learning.

• Meila, Marina

  http://www.stat.washington.edu/mmp/
  Graphical models, learning in high dimensions, tree networks.

• Minka, Thomas P.

  http://research.microsoft.com/~minka/
  Machine learning, computer vision, Bayesian methods.

• Muresan, Raul C.

  http://www.raulmuresan.home.ro/
  Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.

• Murphy, Kevin P.

  http://www.cs.berkeley.edu/~murphyk
  Graphical models, machine learning, reinforcement learning.

• Murray-Smith, Roderick

  http://www.dcs.gla.ac.uk/~rod/
  Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.

• Neal, Radford

  http://www.cs.toronto.edu/~radford
  Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.

• Oja, Erkki

  http://www.cis.hut.fi/oja/
  Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.

• Olshausen, Bruno

  http://redwood.berkeley.edu/bruno
  Visual coding, statistics of images, independent components analysis.

• Paccanaro, Alberto

  http://homes.gersteinlab.org/people/alberto/
  Learning distributed representation of concepts from relational data.

• Pearlmutter, Barak

  http://www-bcl.cs.may.ie/~barak/
  Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.

• Peterson, Leif E.

  http://myprofile.cos.com/petersoL16
  Researcher at Methodist Hospital Research Institute on classification technology and related fields.

• Prashant, Joshi

  http://fias.uni-frankfurt.de/~joshi/
  Computational Neuroscientist. Research interests: reservoir computing, computational motor control, computation with spiking neurons.

• Rao, Rajesh P. N.

  http://www.cs.washington.edu/homes/rao/
  Models of human and computer vision.

• Rasmussen, Carl Edward

  http://learning.eng.cam.ac.uk/carl/
  Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.

• Roberts, Stephen

  http://www.robots.ox.ac.uk/~sjrob
  Machine learning and medical data analysis, independent component analysis and information theory.

• 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.

• Roweis, Sam T.

  http://www.cs.nyu.edu/~roweis/
  Speech processing, auditory scene analysis, machine learning.

• Russell, Stuart

  http://www.cs.berkeley.edu/~russell/
  Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.

• Rutkowski, Leszek

  http://www.kik.pcz.czest.pl/~rutkowski/
  Neural networks, fuzzy systems, computational intelligence.

• Saul, Lawrence K.

  http://www.cs.ucsd.edu/~saul/
  Machine learning, pattern recognition, neural networks, voice processing, auditory computation.

• Storkey, Amos

  http://homepages.inf.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.

• 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.

• Tishby, Naftali

  http://www.cs.huji.ac.il/~tishby/
  Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.

• Versace, Massimiliano

  http://www.maxversace.com/
  Neural networks applied to visual perception and computational modeling of mental disorders.

• Wainwright, Martin

  http://www.eecs.berkeley.edu/~martinw/
  Statistical signal and image processing, natural image modelling, graphical models.

• Wallis, Guy

  http://www.uq.edu.au/~uqgwalli/
  Object recognition, cognitive neuroscience, interaction between vision and motor movements.

• 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.

• 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.

• Wu, Yingnian

  http://www.stat.ucla.edu/~ywu/
  Stochastic generative models for complex visual phenomena.

• Xiaoguang, Rui

  http://sites.google.com/site/rxghome/
  Researcher at University of Science and Technology of China. About image annotation, image retrieval, social network analysis, pattern recognition and machine learning.

• Xing, Eric

  http://www.cs.cmu.edu/~epxing/
  Statistical learning, machine learning approaches to computational biology, pattern recognition and control.

• Zemel, Richard

  http://www.cs.utoronto.ca/~zemel/
  Unsupervised learning, machine learning, computational models of neural processing.

• Zhou, Zhi-Hua

  http://cs.nju.edu.cn/zhouzh/
  Neural computing, data mining, evolutionary computing, ensemble networks.

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