Huiqiang's research primarily concentrates on efficient methods to accelerate inference or training, including dynamic sparse attention (MInference), prompt compression (LLMLingua), KV-cache compression, speculative decoding, model compression, sparse inference (PIT), neural architecture search (NAS), and efficient tuning, with a particular emphasis on LLMs. Additionally, he is interested in addressing typical challenges in natural language processing.