# CAREER: Advancing Distributed Data Compression and Communication via Generative Models, Learning, and Information Theory

> **NSF 01002829DB NSF RESEARCH & RELATED ACTIVIT** · University of Texas at Austin (TX) · $647,797

## Abstract

Modern data-intensive applications, such as the Internet of Things (IoT), virtual reality, and cooperative robotics, generate large amounts of correlated data from distributed devices, making it crucial to communicate this data efficiently to optimize task performance. Two essential components in achieving this are: (1) data compression methods that effectively leverage the correlated properties of the data and are tailored to specific tasks; and (2) efficient and reliable communication algorithms designed for networked and complex communication systems. This project aims to develop innovative frameworks for constructing compression and communication algorithms to address these needs. By integrating insights from information and coding theory with modern techniques such as generative models and deep learning, the project will establish novel methodologies to drive the discovery of new algorithms. This interdisciplinary project will integrate research with several outreach and educational activities, including interactive demonstrations and educational initiatives for K-12 students, the incorporation of research findings into academic courses, engagement in research community events, collaborations with industry, and the broad dissemination of project outcomes through a tutorial blog and open-source libraries.

The overarching goal of this project is to establish frameworks that integrate information theory and learning to develop new compression and communication algorithms

## Key facts

- **NSF award ID:** 2443857
- **Awardee organization:** University of Texas at Austin (TX)
- **SAM.gov UEI:** V6AFQPN18437
- **PI:** Hyeji Kim
- **Primary program:** 01002829DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Machine Learning Theory, CAREER-Faculty Erly Career Dev, COMM & INFORMATION THEORY, WOMEN, MINORITY, DISABLED, NEC
- **Estimated total:** $647,797
- **Funds obligated:** $243,105
- **Transaction type:** Continuing Grant
- **Period:** 07/01/2025 → 06/30/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2443857

## Citation

> US National Science Foundation, Award 2443857, CAREER: Advancing Distributed Data Compression and Communication via Generative Models, Learning, and Information Theory. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2443857. Licensed CC0.

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