Foundations for Efficient Information Usage

Abstract:
A defining feature of the modern information age is the widespread adoption of technologies which rely on, and generate, vast amounts of data. This ‘data deluge’ puts a corresponding burden on computing infrastructure supporting these algorithms, which must store, communicate, and infer from this data. Thus, a key challenge is to develop techniques for reducing this burden by making efficient use of the information contained in the data. This talk looks at this from a theoretical viewpoint.In the first part of the talk, I will discuss theoretical results on Kullback-Leibler (KL) divergence coverings, and show how these theorems can be used to solve key problems when the data we work with are probabilities. We establish stronger bounds in a variety of application areas such as data storage on DNA, online learning with patterns, and quantization of probability vectors.

I will also discuss work on investigating the phenomenon of social pressure using an agent-based opinion dynamics model, in which social pressure may cause agents in a network to lie about their true beliefs. In this model, social pressure may be sufficient to cause the entire network to appear to converge to agreement even though some agents secretly disagree; however, we prove that, under any conditions, all the agents’ true opinions can still be inferred from their behavior.

Bio:
Jennifer Tang is a Postdoctoral Associate at MIT in the Institute of Data, Systems and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS), working with Professor Ali Jadbabaie. She received her Ph.D and S.M in EECS at MIT, advised by Yury Polyanskiy and a B.S.E in Electrical Engineering from Princeton University.  Jennifer won the 2022 ISIT Best Student Paper Award. She also has received the Irwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowship at MIT and is the Shannon Centennial Celebration Student Competition Winner. Her research focuses on finding theoretical guarantees on problems in information and data science, at the intersection of information theory, applied probability, networks, and collective social phenomena.