Applied Random Matrix Theory

Monday, April 23, 2018 - 3:35pm - 4:35pm
Keller 3-180
Joel Tropp (California Institute of Technology)
Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. Therefore, it is desirable to have tools for studying random matrices that are flexible, easy to use, and powerful. Over the last fifteen years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that balance these criteria. This talk offers an invitation to the field of matrix concentration inequalities and their applications.

Joel A. Tropp is Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology. He earned the Ph.D. degree in Computational Applied Mathematics from the University of Texas at Austin in 2004. His research centers on signal processing, numerical analysis, and random matrix theory. Prof. Tropp won a PECASE in 2008, and he has received society best paper awards from SIAM in 2010, EUSIPCO in 2011, and IMA in 2015. He has also been recognized as a Highly Cited Researcher in Computer Science each year from 2014–2017.