This month we have Dr. Sri Priya Ponnapalli presenting "Higher-Order Generalized Singular Value Decomposition for Comparative Analysis of Large-Scale Datasets".
Here's the combined abstract and bio:
Dr. Sri Priya Ponnapalli received her Ph.D. in Electrical and Computer Engineering in 2010 from the University of Texas at Austin, working in the Genomic Signal Processing Lab of Dr. Orly Alter, USTAR Associate Professor of Bioengineering and Human Genetics at the SCI Institute. In her Ph.D. dissertation, Dr. Ponnapalli developed a novel mathematical framework for the comparison of multiple large-scale datasets that are arranged in tables of different row dimensions but the same column dimensions. The number of such datasets, recording different aspects of a single phenomenon, is fast growing in science and medicine. Gaining access to the full information that these datasets store requires mathematical frameworks that can compare and contrast them in order to find the similarities and dissimilarities among them. Until now only one such framework existed, which was limited to a comparison of two datasets at a time.
Ponnapalli and Alter, in collaboration with Drs. Charles F. Van Loan of Cornell University and Michael A. Saunders of Stanford University, formulated a novel generalization of the existing framework that enables comparison of more than just two datasets at a time. The team demonstrated the novel framework in comparative modeling of the cellular activities of three evolutionarily disparate organisms – human and budding and fission yeasts. The mathematical model successfully identified and separated cellular events that are common to the human and yeasts from those that are exclusive to only one of the organisms. (To read the 2011 PLoS One article, visit http://dx.doi.org/10.1371/journal.pone.0028072 ).
This talk will present the comparative mathematical framework formulated by Ponnapalli in her Ph.D. dissertation. Although this framework was developed with applications in biotechnology in mind, it could similarly be used to make discoveries in any of the many areas where large-scale datasets are being accumulated today.
Ponnapalli is now part of the R&D Machine Learning group at Bloomberg. (To read more, visit http://www.sci.utah.edu/people/alumni/694-priya.html ).