Communication and Information Theory
Speaker: Dr. Lele Wang
Affiliation: Stanford University
Seven decades ago, Shannon established the fundamental limits of point-to-point communication. Since then, designing explicit code structures with low encoding/decoding complexity for practical communication systems has been an active research area. Recently, two classes of low-complexity codes—polar codes and spatially coupled LDPC codes— were shown to achieve the theoretical limits of point-to-point communication. However, in various other communication and detection scenarios, despite decades of research, there is still a gap between the theoretical limit and what is practically achievable at low computational complexity.In this talk, I will showcase how to design appropriate code structures in various distinct communication and detection problems, including interference management, universal channel coding, and phase detection for positioning systems. Leveraging tools from information theory, coding theory, communication theory, graph theory and combinatorics, these schemes achieve optimal theoretical rates while maintaining an implementable encoding/decoding complexity. I will conclude by briefly discussing future research directions in emerging data science applications through the lens of structure and complexity.
Lele Wang is a postdoctoral researcher in Electrical Engineering at Stanford University. She spent one year at Tel Aviv University before joining Stanford, also as a postdoctoral researcher. She received the Ph.D. degree in Communication Theory and Systems at the University of California, San Diego. She attended the Academic Talent Program and obtained the B.E. degree at Tsinghua University. Her research interests include information theory, coding theory, communication theory, graph theory, and combinatorics. She is a recipient of the 2013 UCSD Shannon Memorial
Fellowship, the 2013-2014 Qualcomm Innovation Fellowship, and the 2017 NSF Center for Science of Information (CSoI) Postdoctoral Fellowship. Her Ph.D. thesis “Channel coding techniques for network communication” won the 2017 IEEE Information Theory Society Thomas M. Cover Dissertation Award.