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The O’Donnell Institute scientists are developing several AI-enabled data compression algorithms and testing them on SMU’s HPC platforms. Data compression is a key research area focused on reducing the size of digital data, such as text, images, video, or audio, to enable faster transmission and more efficient storage. Unlike traditional rule-based techniques that depend on fixed statistical models, modern AI/ML methods use neural network and deep learning architectures including Convolutional Neural Networks (CNNs), Autoencoders, Transformers, and Generative Adversarial Networks (GANs) to learn complex patterns directly from large datasets. These approaches achieve higher compression ratios and improved perceptual quality compared to classical algorithms by capturing both local and global dependencies. AI-enabled data compression has gained momentum in recent years due to its impact on optimizing communication, lowering infrastructure costs, and enabling data-intensive applications in fields such as medical imaging, satellite communications, and multimedia streaming.