Education & Postdoctoral Training
Research Fellow, Harvard Medical School & Boston Children’s Hospital, Boston, MA, USA, 2017-2021
Postdoctoral Associate, Dept. of EECS, University of Kansas, Lawrence, KS, USA, 2015-2017
Ph.D., Dept. of Electronic Engineering, Tsinghua University, Beijing, China, 2010-2015
M. Eng., School of Software and Microelectronics, Peking University, Beijing, China, 2005-2007
B. S., Dept. of Physics, China Agricultural University, Beijing, China, 2000-2004
Academic Appointment & Hospital Appointment
Assistant Professor, National Institute of Health Data Science, Peking University, 2022
Instructor of Radiology, Faculty of Medicine of Harvard University, Harvard Medical School, 2021-2022
Scientist, Dept. of Radiology, Boston Children’s Hospital, 2021-2022
Software Architect/Technical Advisor, Sogou Inc., Beijing, China, 2007-2013
Software Engineer, Yahoo! Research, Beijing, China, 2004-2005
Major Committee Assignments
Senior Program Committees
AAAI 2022 -
Program Committees
CVPR 2019 - 2022
ICCV 2019 - 2021
ECCV 2020 -
ICLR 2022 -
NeurIPS 2022 -
MICCAI 2020 - 2022
AAAI 2019 - 2021
ACCV 2020 -
WACV 2021 - 2022
ICIP 2017 - 2021
Narrative Report of Research Contributions
I am an Assistant Professor at National Institute of Health Data Science, Peking University, with research interests in a broad range of medical and engineering disciplines, including specific expertise and research experience in neuroimaging, medical image computing, machine learning, computer vision, and artificial intelligence. The goal of my research is to advance our understanding of the structure and function of the brain for improving our capacity to diagnose and treat disease. My research to date has focused on developing novel technologies and computational image models, in order to understand and interpret radiological images. The most significant accomplishments of my research have been the development of novel paradigms and algorithms for fundamentally new approaches to analyze and interpret images. I have set up new research studies to develop cutting-edge techniques of artificial intelligence, machine learning, computer vision, and signal processing. These studies provide solid theoretical and technical bases in my research in medical imaging. Many of my algorithmic developments have introduced entirely new approaches in the field, which have been adopted by others nationally and internationally as a basis for new directions for development. These studies have resulted in 30+ peer-reviewed publications in premium journals and conferences, among which 20+ were first-authored.
I am highly motivated to develop and translate novel imaging technologies into clinical practices. Major applications have included real-time motion-robust MRI acquisition, and motion-compensated MRI reconstruction, to support image-guided surgery, fast and high-quality brain MRI, and quantitative image analysis, to detect morphological change for preoperative assessment of seizure foci and normal function in pediatric epilepsy patients.
I have successfully collaborated with multi-disciplinary research teams and industry to develop new imaging techniques and software tools. My research has been characterized by fundamental contributions to the basic science of imaging and medical image analysis, and collaboration with clinicians to translate those contributions into dramatic impact in clinical and translational research.
Peer-Reviewed Publications
2022
1.Yao Sui, Onur Afacan, Camilo Jaimes, Ali Gholipour, and Simon K. Warfield. “Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction.” IEEE Transactions on Medical Imaging (TMI), 2022, 1-17.
2.N. Ceren Askin, Yao Sui, Laura Gui Levy, Laura Merlini, Joana Sa de Almeida, Sebastien Courvoisier, Tess E. Wallace, Antoine Klauser, Onur Afacan, Simon K. Warfield, Petra Hüppi, and Francois Lazeyras. “Super-Resolution Reconstruction of T2-Weighted Thick-Slice Neonatal Brain MRI Scans.” Journal of Neuroimaging, 2022, 32(1): 68-79.
