Abstract: Information theory, the mathematical theory of communication, is concerned with the most efficient way to communicate in the presence of noise. In this talk, I will discuss a "sequence variant" of information theory, where instead of efficient communication, one is interested in fastest timing recovery. To motivate the discussion, I will begin by performing a mind-reading card trick that is robust to bluffing volunteers. I will demonstrate how the theory behind this trick can be used to design sequences for fastest detection in positioning systems under various scenarios, including single sequence detection, adaptive resolution detection, and simultaneous multi-phase detection. Leveraging tools from information theory, algebraic coding, probabilistic combinatorics, and graph theory, I will develop the fundamental limits associated with these problems, and provide explicit optimal sequence constructions.
Bio: 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. As a member of the Academic Talent Program, she obtained the B.E. degree in Electrical Engineering at Tsinghua University. Her research interests include information theory, coding theory, and communication theory. 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.
Host: Muriel Medard