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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 the University of Sussex 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 to 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 is the director of the programme on 'Neural Computation and Adaptive Perception', which is funded by the Canadian Institute for Advanced Research.
Geoffrey 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) and the NSERC Herzberg Medal, which is Canada's top award in Science and Engineering.
Geoffrey 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, variational learning, products of experts and deep belief nets. His current main interest is in unsupervised learning procedures for multi-layer neural networks with rich sensory input.