Ye Liu

NeuroLip proposes an event-based spatiotemporal framework for visual speaker recognition that achieves robust cross-scene generalization by capturing fine-grained lip dynamics, outperforming existing methods by over 8%.

The paper proposes a scalable, helper-data-free open-set framework using an OpenGAN-based classifier to unify authentication for diverse and large populations of heterogeneous PUF-based IoT devices.

The paper introduces TEERepair, a framework that automatically repairs severe security vulnerabilities caused by improper partitioning in Trusted Execution Environments (TEEs) by combining a domain-specific language (DSL) with large language models (LLMs).

The paper introduces SymTEE, an LLM-assisted symbolic execution framework that detects missing input validation vulnerabilities in TEE applications without needing complex, real TEE setups.

The paper introduces Autonomous Agentic Data Engineering, demonstrating that LLMs can autonomously plan and optimize end-to-end data curation pipelines, leading to substantial performance gains in specialized models.