Yuncheng Wu

SGN enables effective data generation for shifted target domains without the need for retraining, transforming how we approach data augmentation in machine learning.

Intermediate embeddings in distributed MLLM frameworks can leak sensitive image prompts, with new attacks achieving 100% extraction accuracy and revealing serious privacy implications.

Integrating AI into databases introduces a complex web of challenges, from query optimization to security, that demand a fundamental rethinking of traditional database architectures.