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Honda Prize Laureates

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CURRICULUM VITAE Of Dr. Geoffrey E. Hinton

Born

December 1947, UK (British & Canadian citizenship)

Education and Training
1970: Cambridge University, BA in Experimental Psychology
1978: University of Edinburgh, Ph.D. in Artificial Intelligence

Employment History
1978-80: University of California, San Diego, Postdoctoral Fellow
1980-82: MRC Applied Psychology Unit, Cambridge, Research Scientist
1982-87: Carnegie Mellon University, Assistant & Associate Professor
1987-current: University of Toronto, Professor, Computer Science Department
1998-2001: University College London, Director, Gatsby Computational Neuroscience Unit
2013-current: Google Brain Team, Engineering Fellow
2016-current: Vector Institute, Chief Scientific Adviser (pro bono)

Biographical Sketch
Dr. Geoffrey Hinton has been researching artificial neural networks for 47 years. Working with David Rumelhart and Ronald Williams, he showed that deep neural networks can learn useful distributed representations of complex data. Working with Terry Sejnowski, he invented Boltzmann machines. His other contributions include mixtures of experts, variational inference and learning for neural networks, Deep Belief Nets and Capsule Nets. His research group at the University of Toronto used deep learning to revolutionize speech recognition in 2009 and object classification in 2012.
Honors and Awards
1998: Fellow of the Royal Society
2001: David E. Rumelhart Prize in Cognitive Science
2005: IJCAI Award for Research Excellence in Artificial Intelligence
2011: Herzberg Canada Gold Medal for Science and Engineering
2016: Foreign Member of American National Academy of Engineering
2016: IEEE/RSE James Clerk Maxwell Gold Medal
2016: BBVA Foundation of Knowledge Award, Information and Communication Technologies
2016: NEC C&C Award
2018: ACM Turing Award (with Yann LeCun and Yoshua Bengio)

Major Publications
A Learning Algorithm for Boltzmann Machines: (with Ackley, D. H. & Sejnowski, T. J.), Cognitive Science, Elsevier, 9 (1): 147-169, 1985
Learning Representations by Back-Propagating Errors: (with Rumelhart, D. E. & Williams, R.J.) Nature 323 (6088): 533-536, 9 October 1986
A Fast Learning Algorithm for Deep Belief Nets: (with Osindero, S. & Teh, Y.) Neural Computation, 18 (7): 1527-1554, July 2006
Reducing the Dimensionality of Data with Neural Networks: (with Salakhutdinov, R.R.) Science, 313 (5786): 504-507, 28 July 2006
ImageNet Classification with Deep Convolutional Neural Networks: (with Krizhevsky, A. & Sutskever, I.), NIPS 2012. Curran Associates Inc.: 1097-1105, 3 December 2012

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