Abstract: Tensor decompositions are generalizations of low-rank matrix approximations to higher dimensional data. They have become popular for their utility across applications—including blind source separation, dimensionality reduction, compression, anomaly detection—where the original data is represented as a multidimensional array. We’ll highlight a few applications where tensor decompositions, such as CP, Tucker, and Tensor Train decompositions, are particularly effective. We’ll discuss properties of the various decompositions, and we’ll describe the algorithms used to compute them. In particular, we’ll see how to apply randomized algorithms to reduce computational costs of the algorithms with minimal degradation of accuracy.

Short Bio: Grey Ballard is an Associate Professor in the Computer Science Department at Wake Forest University. After receiving his PhD in computer science from the University of California Berkeley in 2013, he was a Truman Fellow at Sandia National Laboratories in Livermore, CA. He received his BS in math and computer science at Wake Forest in 2006 and his MA in math at Wake Forest in 2008.
His research interests include numerical linear algebra, high performance computing, and computational science, particularly in developing algorithmic ideas that translate to improved implementations and more efficient software. His work has been recognized with the Wake Forest Excellence in Research Award; an NSF CAREER award; the SIAM Linear Algebra Prize; three conference best paper awards, at SPAA, IPDPS, and ICDM; the C.V. Ramamoorthy Distinguished Research Award at UC Berkeley; and the ACM Doctoral Dissertation Award - Honorable Mention.