Qian Yang is an Assistant Professor in Computing and Information Science at Cornell University. Yang’s research focuses on improving human-AI complementarity in knowledge work. Her digital pathology system design improved the pathologist-AI teams’ cancer diagnostic accuracy by 30%. Yang’s recent work focuses on human collaboration with Foundational Models. Yang is an ACM SIGCHI Outstanding Dissertation Award winner and co-lead of the Digital and AI Literacy Initiative (DALI) at Cornell.
Generative models (e.g., GPT-3 and Dall-E) have the potential to boost humanities’ most knowledge-intensive, creatively-oriented tasks, from parsing scientific literature to writing fictions and poetry. Through this fellowship, Qian’s research aims to assess generative AI’s impact on people’s cognitive processes in performing such tasks and to create novel generative AI applications that enhance rather than automate their thinking.
AI2050 Community Perspective — Qian Yang (2023)
J.D. Zamfirescu-Pereira, H. Wei, A. Xiao, K. Gu, G. Jung, M. Lee, B. Hartmann, and Q. Yang. Herding AI cats: lessons from designing a chatbot by prompting GPT-3. ACM DIS. 2023.
J.D. Zamfirescu-Pereira, R. Wong, B. Hartmann, and Q. Yang. Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. ACM CHI. 2023.
Q. Yang, Y. Hao, K. Quan, and S. Yang. Harnessing biomedical literature to calibrate clinicians’ trust in AI decision support systems. ACM CHI. 2023.
M. Lin, B. Hou, S. Mishra, T. Yao, Y. Huo, Q. Yang, F. Wang, G. Shih, and Y. Peng. Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access. Computers in Biology and Medicine. 2023.
H. Sandhaus, W. Ju, and Q. Yang. Towards prototyping driverless vehicle behaviors, city design, and policies simultaneously. arXiv. 2023.