Statistical Learning Methods for Big Data Analysis and Predictive Algorithm Development

Date and Time: 
2013 Monday, April 1
CG1 Auditoriums
John Williams

Authors: John K. Williams, David Ahijevych, Gary Blackburn, Jason Craig and Greg Meymaris

As the volume of observational and model data continue to grow, it is increasingly necessary to develop new automated methods to find patterns and predictive relationships.  This talk will summarize some of the methods currently being developed in the environmental sciences, including highlights of some of the talks presented at the Conference on Intelligent Data Understanding held at NCAR last fall.  It will also present analysis methods and tools used to extract knowledge and develop skillful predictive algorithms for aviation turbulence and convection.

Speaker Description: 

Dr. John Williams is a Project Scientist II in NCAR’s Research Applications Laboratory, where he has 16 years of experience in researching, developing and deploying aviation weather algorithms, several of which have utilized statistical learning techniques. He holds an M.S. in Applied Mathematics and a Ph.D. in Mathematics from the University of Colorado. As a member and chair of the American Meteorological Society’s Committee on Artificial Intelligence Applications to Environmental Science, Dr. Williams organized several conferences, workshops, forecasting contests and an educational forum. He was also on the organizing committee for the Conference on Intelligent Data Understanding hosted at NCAR last fall.

PDF icon StatLearnBigData20130401.pdf3.77 MB
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