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Geoffrey E. Hinton's Biographical Sketch
Geoffrey Hinton received his BA in experimental psychology from
Cambridge in 1970 and his PhD in Artificial Intelligence from
Edinburgh in 1978. He did postdoctoral work at Sussex
University and the University of California San Diego and spent five
years as a faculty member in the Computer Science department at
Carnegie-Mellon University. He then became a fellow of the Canadian
Institute for Advanced Research and moved to the Department of Computer Science
at the University of Toronto. He spent three years from 1998 until
2001 setting up the Gatsby
Computational Neuroscience Unit at University College London and
then returned to the University of Toronto where he is a University
Professor. He holds a Canada Research Chair in Machine
Learning. He is the director of the program on "Neural Computation
and Adaptive Perception" which is funded by the Canadian Institute for Advanced
Research.
Geoffrey Hinton is a fellow of
the Royal Society,
the Royal Society of Canada,
and
the Association for
the Advancement of Artificial Intelligence.
He is an honorary foreign member of
the American Academy of Arts and Sciences,
and a former president of
the Cognitive
Science
Society. He received an honorary doctorate from the
University of Edinburgh in 2001. He was awarded the first
David
E. Rumelhart prize (2001), the
IJCAI award for
research excellence (2005), the IEEE Neural Network Pioneer award
(1998) and the ITAC/NSERC award for contributions to information
technology (1992).
A simple introduction to Geoffrey Hinton's research can be found in
his articles in Scientific American in September 1992 and October
1993. He investigates ways of using neural networks for learning,
memory, perception and symbol processing and has over 200
publications in these areas. He was one of the researchers who
introduced the back-propagation
algorithm that has been widely used for practical applications. His
other contributions to neural network research include Boltzmann machines, distributed representations, time-delay
neural nets, mixtures of experts, Helmholtz machines and products of experts. His
current main interest is in unsupervised learning procedures for
neural networks with rich sensory input.
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