When
Wednesday, November 19, 2025, at 2:00 p.m.
Krishna Narayanan
Professor of Electrical and Computer Engineering
Texas A&M University
"Revisiting Compression Through the Lens of Generative AI"
ECE 530 | Zoom link
Abstract: The classical insight from information theory that prediction, learning and compression are intimately connected has gained renewed relevance in light of recent advances in generative artificial intelligence. Large language models and diffusion processes have demonstrated remarkable capabilities in capturing and synthesizing complex data distributions across modalities including language, audio and images. These developments prompt a re-examination of fundamental problems in compression and communication, particularly in the context of joint source-channel coding. The signal processing, communications and information theory communities are increasingly investigating how generative models can be leveraged not only to model sources more effectively, but also to reimagine encoders, decoders and estimation algorithms. In this talk, I will survey recent results at this intersection, focusing mostly on algorithmic innovations.
Bio: Krishna Narayanan is the Sanchez Chair Professor in the Department of Electrical and Computer Engineering at Texas A&M University. He recently held visiting positions at Qualcomm research and at the Simons Institute for Theory of Computing. His research interests are broadly in coding theory, information theory, signal processing and machine learning with applications to wireless communications. He recently received the 2022 joint communications society and information theory best paper award, the 2020 best paper award in data storage from the IEEE communications society and a university-level distinguished teaching award in 2018. He has served as a lecturer at the North American, Australian and East Asian schools on information theory. He was elected a Fellow of the IEEE for contributions to coding for wireless communications and data storage.