Current challenges in statistical learning (11w5051)


(Acadia University)

Xiaotong Shen (University of Minnesota)

(Stanford University)

(The Ohio State University)

(University of Waterloo)

Ji Zhu (University of Michigan)


The Banff International Research Station will host the "Current challenges in statistical learning" workshop from December 11th to December 16th, 2011.

This workshop will bring together researchers in statistics to discuss ideas for automated learning and data mining that will enhance the discovery process throughout the sciences. Recent developments in machine learning, and the widespread need for knowledge discovery, provide an excellent opportunity to bring researchers together to address important issues of the analysis of high-dimensional data as well as the data of complex structures, which arise in information technology and biomedical sciences, as well as in many other fields. The workshop will cover a range of topics, including support vector machines, boosting, the self-organization maps, structured learning, unsupervised and semisupervised learning and other nonlinear classification methods, as well as application areas of microarrays, bio/chemo informatics, and text mining. We anticipate a lively exchange among senior and junior researchers, which should lead to productive collaborations, especially among young researchers.

The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).