Structure in Information Theory: Codes, Sequences, and Graphs

UC San Diego alumna Lele Wang (Ph.D.


Date: 2018-02-08
Time: 1pm-2pm
Location: Room 2512, Jacobs Hall, UC San Diego

Guest Speaker: Lele Wang, Stanford University
Postdoctoral Research and UC San Diego Alumna

Host: ECE/ITA professor Young-Han Kim


In this talk, Stanford University postdoctoral research in electrical engineering Lele Wang will present three distinct problems in information theory, in which embedding appropriate structures plays a key role in finding optimal solutions. First, she will propose a universal polarization technique, which alleviates the limitation of Arikan's channel-specific polar code design. Next, Wang will describe how to design binary sequences such that the location of any length-$k$ chunk in the sequence can be uniquely determined via a noisy observation of the chunk. Such sequences can be used for phase detection in positioning systems. After that, Wang will introduce a notion called graph information ratio, which unifies Shannon capacity of a graph and fractional chromatic number. A distance measure can be defined via graph information ratio, which characterizes the similarity between graphs. Finally, the UC San Diego alumna (Ph.D. '15) 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. 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.