Dr. James Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering and a Professor of Radiology & Biomedical Imaging, Electrical Engineering and Statistics & Data Science at Yale University, where he has been since 1983. Dr. Duncan received his B.S.E.E. with honors from Lafayette College (1973), and his M.S. (1975) and Ph.D. (1982) both in Electrical Engineering from the University of California, Los Angeles. At Yale, he currently serves as Chair and previously as Director of Undergraduate Studies for Biomedical Engineering. Dr. Duncan’s research efforts have been in the areas of computer vision, image processing, machine learning and medical imaging, with an emphasis on biomedical image analysis. He has published over 250 peer-reviewed articles in these areas and has been the principal investigator on a number of peer-reviewed grants from both the National Institutes of Health and the National Science Foundation over the past 35 years. He is a Fellow of the Institute of Electrical and Electronic Engineers (IEEE), of the American Institute for Medical and Biological Engineering (AIMBE) and of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. In 2014 he was elected to the Connecticut Academy of Science & Engineering. He has served as co-Editor-in-Chief of Medical Image Analysis, as an Associate Editor of IEEE Transactions on Medical Imaging, and on the editorial board of Pattern Analysis and Applications, Journal of Mathematical Imaging and Vision, and “Modeling in Physiology” The American Physiological Society. He is a past President of the MICCAI Society. In 2012, he was elected to the Council of Distinguished Investigators, Academy of Radiology Research and in 2017 received the “Enduring Impact Award” from the MICCAI Society.
What do you do with data science?
Data driven learning for classification and regression problems in medical imaging/ medical image analysis, including for image segmentation, motion tracking and functional imaging.
X. Li, Y. Zhou, N. Dvornek, M. Zhuang, S. Gao, J. Zhuang, D. Scheinost, L.H. Staib, P. Ventola and J. S. Duncan, “BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis,” Medical Image Analysis, Vol. 74, December, 2021 Pp. 1-13. (in press). Available online at https://doi.org/10.1016/j.media.2021.102233
B. Zhou, C. Liu and JS Duncan, “Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth,” International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS Vol. 12901, September, 2021. Pp. 47-56.
J. Zhuang, Y. Ding, T. Tang, N. Dvornek, S. Tatikonda and J. S. Duncan, “Mo- mentum Centering and Asynchronous Update for Adaptive Gradient Methods,” 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Aus- tralia. 2021. Pp. 1-12.
Juntang Zhuang, Sekhar Tatikonda, and James S Duncan, “Multiple-shooting Ad- joint Method for Whole- Brain Dynamic Causal Modeling, ” Information Processing in Medical Imaging (IPMI2021), LNCS Vol. 12729, Denmark, June 2021. Pp. 58-70.
Allen Lu, Shawn S. Ahn, Kevin Ta, Nripesh Parajuli, John C. Stendahl, Zhao Liu, Nabil E. Boutagy, Geng-Shi Jeng, Lawrence H. Staib, Matthew O’ Donnell, Albert J. Sinusas, and James S. Duncan, “Learning-based Regularization for Cardiac Strain Analysis via Domain Adaptation,” IEEE Transactions on Medical Imaging, Vol. 40, No. 9, September, 2021. Pp. 2233 – 2244.