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Garrison Cottrell | |
|---|---|
| Nationality | American |
| Occupation(s) | Computer Scientist, academic, author and researcher |
| Academic background | |
| Education | B.S., Mathematics and Sociology M.A.T., Mathematics Education M.S., Computer Science Ph.D., Computer Science |
| Alma mater | Cornell University University of Rochester |
| Academic advisors | James F. Allen David Rumelhart |
| Academic work | |
| Institutions | University of California, San Diego |
| Notable students | Christopher Kanan |
Garrison Cottrell is an American cognitive scientist, computer scientist, academic, author and researcher. He is a Professor with an appointment in Computer Science and Engineering at the University of California, San Diego.[1]
Cottrell’s research falls into areas encompassing artificial intelligence, neural networks, and cognitive science. He has authored over 200 academic papers.[2]
Education[edit]
Cottrell received his Bachelor's degree in Mathematics and Sociology as well as an M.A.T. in Mathematics Education from Cornell University.[3] He then earned an M.S. and Ph.D. in Computer Science at the University of Rochester where he was advised by James F. Allen.[4] After he completed his Ph.D., he was a postdoctoral scholar with David Rumelhart.[5]
Career[edit]
Following his post-doctoral fellowship, Cottrell joined the Computer Science and Engineering department at the University of California, San Diego as an Assistant Professor. He was promoted to Full Professor in 1997. Since 2000, he has served as the Director of the university's Interdisciplinary Ph.D. Program in Cognitive Science.[6]
Research[edit]
Cottrell has worked throughout his career to advance research in artificial neural networks. Beyond neural networks, he has done extensive work to study how people choose what to view in scenes with saccadic eye movements.
Selected articles[edit]
- Dollár, P., Rabaud, V., Cottrell, G. and Belongie, S., 2005, October. Behavior recognition via sparse spatio-temporal features. In 2005 IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance (pp. 65-72). IEEE.
- Zhang, L., Tong, M.H., Marks, T.K., Shan, H. and Cottrell, G.W., 2008. SUN: A Bayesian framework for saliency using natural statistics. Journal of vision, 8(7), pp.32-32.
- Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G. and Cottrell, G., 2017. A dual-stage attention-based recurrent neural network for time series prediction. arXiv preprint arXiv:1704.02971.
- Valentin, D., Abdi, H., O'Toole, A.J. and Cottrell, G.W., 1994. Connectionist models of face processing: A survey. Pattern recognition, 27(9), pp.1209-1230.
- DeMers, D. and Cottrell, G., 1992. Non-linear dimensionality reduction. Advances in neural information processing systems, 5.
- Cottrell, G.W., 1988. Image compression by back-propagation: An example of extensional programming. Advances in cognitive science, 3, pp.208-240.
References[edit]