ECE COLLOQUIUM | Correlation Mining for Imaging and Multidimensional Signal Processing

March 16, 2015 - 15:40 - 17:00

ECE Colloquium and Distinguished Visitor Talk
Correlation Mining for Imaging and Multidimensional Signal Processing
Alfred O. Hero III, R. Jamison and Betty Williams Professor of Engineering, University of Michigan

Room 2020 / 2030, Fred Kaiser Building
2332 Main Mall, UBC | Directions here
refreshments available from 3:40, talk begins at 4pm

Correlation mining is a class of methods for extracting complex patterns from massive  multivariate datasets, such as spatio-temporal data and images. Many operations on such datasets depend on computing a large number of correlations, including linear prediction,  texture analysis, region growing segmentation,  and Gauss Markov random field modeling. This talk will include a presentation of emerging methods of correlation mining for massive datasets, a discussion of the underlying mathematical theory,  and illustrations of their application to several imaging and multidimensional signal processing applications.

Alfred O. Hero III is the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. From 2008-2013 he was held the Digiteo Chaire d'Excellence at the Ecole Superieure d'Electricite, Gif-sur-Yvette, France. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). Alfred Hero was awarded the University of Michigan Distinguished Faculty Achievement Award (2011). He received the IEEE Signal Processing Society Meritorious Service Award (1998), the IEEE Third Millenium Medal (2000), and the IEEE Signal Processing Society Technical Achievement Award (2014). Alfred Hero was President of the IEEE Signal Processing Society (2006-2008).

His recent research interests are in statistical signal processing, machine learning and the analysis of high dimensional spatio-temporal data. Of particular interest are applications to networks, including social networks, multi-modal sensing and tracking, database indexing and retrieval, imaging, and genomic signal processing.

 

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