Sep 23, 2016
Lopata Hall, Room 101
RandNLA: Randomization in Numerical Linear Algebra
Department of Computer Science
The introduction of randomization in the design and analysis of algorithms for matrix computations (such as matrix multiplication, least-squares regression, the Singular Value Decomposition (SVD), etc.) over the past 15 years provided a new paradigm and a complementary perspective to traditional numerical linear algebra approaches. These novel approaches were motivated by technological developments in many areas of scientific research that permit the automatic generation of large data sets, which are often modeled as matrices.
In this talk, we will outline how such approaches can be used to approximately solve problems such as the Singular Value Decomposition (SVD) of matrices and the CX decomposition. Applications of the proposed algorithms to data analysis tasks (with a particular focus in population genetics) will also be discussed.
Petros Drineas is an Associate Professor at the Computer Science Department of Purdue University. He earned a PhD in Computer Science from Yale University in 2003 and a BS in Computer Engineering and Informatics from the University of Patras, Greece, in 1997. His research interests lie in the design and analysis of randomized algorithms for linear algebraic problems, as well as their applications to the analysis of modern, massive datasets, with a particular emphasis on the analysis of population genetics data.