Coast to Coast Seminar Series: "Applying Machine Learning Methods to Climate Variability"
Department of Earth and Ocean Sciences, University of British Columbia
Date: Sep 21, 2010
Time: 11:30 - 12:30
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Machine learning methods, having originated from computational intelligence (i.e. artificial intelligence) are now ubiquitous in the environmental sciences. Applications of machine learning methods, such as neural networks and support vector machines, to the analysis of climate variability and to short term climate prediction will be presented. Examples include the El Nino phenomenon in the tropical Pacific, and interannual variability in the Canadian winter climate and extreme weather.
William Hsieh obtained from the University of British Columbia his B.S. degree in combined honours mathematics and physics (1976), an M.S. in physics (1978), and a Ph.D. degree in oceanography and physics (1981). He did postdoctoral work at Cambridge University and at the University of New South Wales, before returning to the University of British Columbia, where he eventually became Professor in the Department of Earth and Ocean Sciences and in the Department of Physics and Astronomy, as well as the Chair of the Atmospheric Science Programme. He is currently professor emeritus, with an active research group. Best known for his pioneering work in developing and applying machine learning methods in the environmental sciences, he has over 90 peer-reviewed publications covering areas of climate variability and prediction, machine learning, oceanography, atmospheric science and hydrology. His graduate-level book "Machine Learning Methods in the Environmental Sciences -- Neural Networks and Kernels" (2009) was published by Cambridge University Press.