HypOP (Nature MI'24) proposed a scalable unsupervised learning-based optimization method for a wide spectrum of constrained combinatorial optimization problems with arbitrary cost functions.

SureFED proposed a new peer-to-peer federated learning algorithm which is robust to a variety of data and model poisoning attacks utilizing uncertainty quantification.

AdaGL (DAC'23) proposed an adaptive-learning based graph-allocation algorithm to minimize the communication overhead in distributed graph neural network training.

REMARK-LLM (USENIX Security'24) proposed a watermarking framework for large language model's generated content that has high capacity, high transferability, and robustness.

EmMark (DAC'24) proposed a novel watermarking framework for protecting the intellectual property (IP) of embedded large language models deployed on resource-constrained edge devices.

AutoMarks (MLCAD'24) proposed an automated and transferable watermarking framework that leverages graph neural networks to reduce the watermark search overheads during the placement stage.

ICMarks proposed the first quality-preserving and robust watermarking framework for modern physical design to safeguard IC layout IP.