Foundations for Efficient Information Usage: Inference, Storage, Networks

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https://umich.zoom.us/j/94223356787

Meeting ID: 942 2335 6787

Passcode: 921572

A defining feature of the modern information age is the widespread adoption of technologies such as large language models and social networks 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 this talk, I will discuss work on developing the mathematical and engineering foundations for addressing these challenges, rooted in a technique called Kullback-Leibler divergence covering, and how this information theory perspective can be used to design algorithms and show rigorous bounds for various problems in communication, learning, and efficient information storage.

The second topic I will discuss is on opinion dynamics in social settings. I look at a mathematical model of agents on a network who are affected by social pressure and may not disclose what they really think. Under these dynamics, I analyze how to determine the long term behavior of agents and how to effectively infer their true beliefs, bringing understanding to some of the social behaviors we see.

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.