Prof. Qi Dou has been an Assistant Professor at the Department of Computer Science and Engineering at The Chinese University of Hong Kong since Feb 2020. She is an affiliated member of Hong Kong Centre for Logistics Robotics, CUHK T-Stone Robotics Institute, CUHK Institute of Medical Intelligence and XR. Her research interest is in the interdisciplinary field of artificial intelligence and robotics, with expertise in robotic vision, embodied AI, data-driven simulation, and learning-based robot manipulation. Prof. Dou has published over 200 conference and journal papers, with Google Scholar citations 31k+ and H-index 73. Her research work has earned her a number of international and national awards, including China NSFC Excellent Young Scientists Fund (2023), IEEE EMBS Early Career Achievement Award (2023), Ministry of Education Higher Education Outstanding Scientific Research Output Award — Second-class Award in Natural Sciences (2022), Beijing Science and Technology Award — First-class Award in Science and Technology Progress Award (2022), Hong Kong Young Scientist Award (2018). Her research publications have received a number of prestigious best paper awards, including MedIA-MICCAI Best Paper Award (2017), IEEE TBME Prize Paper Award (2021), IEEE ICRA Best Paper Award in Medical Robotics (2021), IJCARS-MICCAI Best Paper Award (2021 & 2024), MICCAI Young Scientist Publication Award (2022), MICCAI Best Paper Award Finalist (2023 & 2024). Prof. Dou has been active in the research community. She serves as General Co-Chair for MICCAI 2026, Program Co-Chair for MICCAI 2024, IPCAI 2023, MICCAI 2022, and MIDL 2022, Senior Program Committee for IROS 2025, Area Chair for NeurIPS 2024-2025, etc. She is currently serving as the Associate Editor for international journals including IEEE TMI, MedIA, IEEE TBME, and IEEE RAL.

My research is at the interdisciplinary field of medical image analysis, machine learning and robotic surgery intelligence, aiming to create synergistic advancements for innovative intelligent systems that achieve an impact to support delivery of higher-quality medical diagnosis, intervention and education via next-generation healthcare technology. Previous representative works include 3D deep learning for image computing, model generalizability and FL on heterogeneous medical data, surgical video analysis for real-time cognitive assistance, and SurRoL.

Recent focus: 1) Surgical Robotics, 2) Agentic AI Systems for Healthcare Applications, 3) Human Robots for Elderly Care, 4) Emobioied AI, 5) LLMs and Smart XR for Medical Education