Zoom Link: 954-143-37873
We develop nonasymptotic concentration bounds for products of independent random matrices. Such products arise in the study of stochastic algorithms, linear dynamical systems and random walks on groups. Our bounds exactly match those available for scalar random variables and continue the program, initiated by Ahlswede-Winter and Tropp, of extending familiar concentration bounds to the noncommutative setting. Our proof technique relies on geometric properties of the Schatten trace class. Joint work with D. Huang, J. A. Tropp, and R. Ward.
Jonathan Niles-Weed is an Assistant Professor of Mathematics and Data Science at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU, where he is a core member of the Math and Data group. He completed his PhD in Mathematics and Statistics at MIT, under the supervision of Philippe Rigollet. His primary area of interest is statistics, probability and mathematics of data science, in particular statistical optimal transport problems.