Kai Li

Achieve state-of-the-art autonomous UAV exploration performance with 87% less computation by using a hierarchical planner that only invokes global replanning when absolutely necessary.

Forget painstakingly labeling training data: this method learns to segment and separate audio by leveraging external knowledge sources like music scores.

LLMs promise to revolutionize UAVs by enabling advanced environmental understanding, swarm coordination, and high-level task reasoning, paving the way for more adaptive and context-aware aerial operations.

Overcome Alzheimer's speech detection's data scarcity with FAL-AD, a federated learning framework that hits 91.52% accuracy by generating synthetic speech samples and aligning acoustic and textual features.