Workshop on Geometry for Signal Processing and Machine Learning

Colorado State University, Fort Collins CO

Investigators

Abstract

This proposal seeks NSF support for a three-day workshop on Geometry for Signal Processing and Machine Learning to be held October 13?15, 2016 in Estes Park, Colorado. The workshop is being organized by Ali Pezeshki, Colorado State University (PI of this proposal), Doug Cochran, Arizona State University, and Louis Scharf, Colorado State University. The workshop will explore applications that impel manifold modeling, and develop a framework for solving inference problems on manifolds. Specific objectives of the workshop include ? identifying challenging problems of signal processing and machine learning that lie outside the scope of subspace methods, ? identifying models and methods from modern geometry that address these problems, ? identifying a set of open research questions that would inform NSF?s continuing and future investments in signal processing and machine learning. The long-term objective is to develop a theory as broad in its scope and as precise in its methodologies as modern statistical signal processing. Such a theory would extend linear subspace models and augment statistical reasoning with geometrical reasoning.