Biography

Mingyi Hong received his B.E. degree in Communications Engineering from Zhejiang University, China, in 2005, his M.S. degree in Electrical Engineering from Stony Brook University in 2007, and Ph.D. degree in Systems Engineering from University of Virginia in 2011. From 2011 to 2014 he was with the Department of Electrical and Computer Engineering, University of Minnesota, first as a Post-Doctoral Fellow, then a Research Associate and a Research Assistant Professor. He is currently a Black & Veatch Faculty Fellow and an Assistant Professor with the Department of Industrial and Manufacturing Systems Engineering and the Department of Electrical and Computer Engineering (by courtesy), Iowa State University. His research interests are primarily in the fields of large-scale optimization theory, statistical signal processing, next generation wireless communications, and their applications in big data related problems.

Abstract

In this talk we present a powerful algorithmic framework for large-scale optimization, called Block Successive Upper bound Minimization (BSUM). The BSUM includes as special cases many well-known methods for signal processing, communication or massive data analysis, such as Block Coordinate Descent (BCD), Convex-Concave Procedure (CCCP), Block Coordinate Proximal Gradient (BCPG) method, Nonnegative Matrix Factorization (NMF), Expectation Maximization (EM) method and so on. In this talk, various features and properties of the BSUM are discussed from the viewpoint of design flexibility, computational efficiency and parallel/distributed implementation. Illustrative examples from networking signal processing and machine learning are presented to demonstrate the practical performance of the BSUM framework.