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FODAVA: Geometric Aspects of Machine Learning and Visual Analytics

Organizers: V. Koltchinskii
M. Maggioni
H. Park
A. Varshney
Speakers: Misha Belkin, Ohio State University
Gunnar Carlson, Stanford University
Tony Jebara, Columbia University
Gilad Lerman, University of Minnesota, Twin Cities
Sayan Mukherjee, Duke University
Justin Romberg, Georgia Institute of Technology
Clayton Scott, Michigan University
Santosh Vempala, Georgia Institute of Technology
Rene Vidal, The Johns Hopkins University
Website: http://fodava.gatech.edu/node/30

The primary aim of the forum is to bring together researchers in Computer Science, Mathematics, Statistics and related areas working on geometric problems in Machine Learning with a potential impact in Data and Visual Analytics. In the recent years, there has been significant progress in Machine and Statistical Learning in general, the design of algorithms that extract and process information from data sets, and the mathematical understanding of the limits and capabilities of such algorithms. In this forum we will focus on recent trends in Machine Learning that aim at understanding the geometric nature of Machine Learning problems. It has been understood that there are rather subtle geometric structures involved in complex high dimensional data sets that have to be revealed in the process of their analysis and visualization. These structures are often hidden even in the data sets that seemingly have nothing to do with geometry (such data sets are common in many Visual Analytics applications). Novel techniques, theoretical insights, algorithms and computational techniques have been developed along this lines and will be discussed in the forum.

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