Wednesday, March 30, 2016 - 9:00am to 10:00am
KEC 1001

Speaker Information

Xiao (Simon) Li
Postdoctoral Research Scholar
Electrical Engineering and Computer Science
University of California, Berkeley

Abstract

Advances in pervasive sensing and ubiquitous communications have led to an era of data deluge, which imposes critical bottlenecks in terms of data size, processing speed and reliability. Sparsity in data structure offers promise in addressing this challenge of “scale", as evidenced by the success of fields like compressed sensing and sparse learning. However, existing popular methods are typically based on optimization techniques, which scale polynomially with the problem dimension, and are getting increasingly hard to scale. In this talk, I will view a wide range of high-dimensional sensing and learning problems through a novel coding-theoretic lens. Using a simple divide-and-conquer philosophy with modern tools from sparse estimation and coding theory, I will show how to enable scalable acquisition and fast computations that scale sub-linearly with the problem dimension. As motivating examples, I will describe how this coding-theoretic framework can be used for a host of problems from compressed sensing, wireless sensing to machine learning and data analytics. This helps us break the complexity barrier while providing strict performance guarantees, and has the potential to enable "real-time" processing of massive datasets in these applications.

Speaker Bio

Xiao (Simon) Li is currently a postdoctoral scholar in the Department of Electrical Engineering and Computer Science at UC Berkeley. His research interests include high-dimensional structured estimation, statistical learning, statistical signal processing and optimization. Prior to joining UC Berkeley, he received the Ph.D. degree in Electrical and Computer Engineering from UC Davis in 2013, where he received the Anil K. Jain best doctoral dissertation award. He got the M.Phil. degree in Electrical and Electronic Engineering from The University of Hong Kong, and the B.Eng. degree in Electrical Engineering from Sun Yat-Sen (Zhongshan) University in China.