Qingqing Cao

Research Scientist •  Apple AIML.


Seattle, WA, USA

I am a research scientist at Apple AIML. My research interests include efficient NLP, mobile computing, and ML systems. I have focused on building efficient and practical NLP systems for both edge devices and the cloud, such as on-device (visual) question answering and faster Transformer models.

Previously, I was a postdoc in the UW NLP group at the University of Washington. I hold a Ph.D. degree in computer science at Stony Brook University. I was a recipient of the Catacosinos Fellowship at Stony Brook University and a Rising Star in Data Science at the University of Chicago.


Feb, 2024 Glad to be invited to serve as Action Editor / Area Chair for ACL 2024 !
Jan, 2024 BTR was accepted to ICLR as a spotlight paper! 🎊
Nov, 2023 Gave a talk at the Efficient ML workshop hosted by Google Research.

recent publications


  1. APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
    Bowen ZhaoHannaneh Hajishirzi, and Qingqing Cao
    Jan 2024
  2. BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models
    Qingqing CaoSewon MinYizhong Wang, and Hannaneh Hajishirzi
    In The Twelfth International Conference on Learning Representations, Jan 2024


  1. PuMer: Pruning and Merging Tokens for Efficient Vision Language Models
    Qingqing CaoBhargavi Paranjape, and Hannaneh Hajishirzi
    In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2023