Leslie Ying

Affiliations: 
Engineering University of Wisconsin-Milwaukee, Milwaukee, WI 
 State University of New York, Buffalo, Buffalo, NY, United States 
Area:
Electronics and Electrical Engineering
Website:
https://engineering.buffalo.edu/ee/faculty/faculty_directory/leslie-ying.html
Google:
"Leslie Ying"
Bio:

aka Lei Ying
https://www.proquest.com/openview/7a7d65cf32b6aa559efd2f727231b125/1

Parents

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David C. Munson grad student 1997-2003 UIUC (Physics Tree)
 (A study of 2-D phase unwrapping)

Children

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Jawad F. Al-Asad grad student 2009 UW-Milwaukee
Yuchou Chang grad student 2012 UW-Milwaukee
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Publications

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Huang P, Zhang C, Zhang X, et al. (2023) Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising. Ieee Transactions On Medical Imaging
Guan Y, Li Y, Liu R, et al. (2023) Subspace Model-Assisted Deep Learning for Improved Image Reconstruction. Ieee Transactions On Medical Imaging
Zhou Y, Wang H, Liu Y, et al. (2022) Accelerating MR Parameter Mapping Using Nonlinear Compressive Manifold Learning and Regularized Pre-Imaging. Ieee Transactions On Bio-Medical Engineering
Huang W, Ke Z, Cui ZX, et al. (2021) Deep low-Rank plus sparse network for dynamic MR imaging. Medical Image Analysis. 73: 102190
Cheng J, Cui Z, Huang W, et al. (2021) Learning data consistency and its application to dynamic MR imaging. Ieee Transactions On Medical Imaging
Ke Z, Huang W, Cui ZX, et al. (2021) Learned Low-rank Priors in Dynamic MR Imaging. Ieee Transactions On Medical Imaging
Gaire SK, Wang Y, Zhang HF, et al. (2021) Accelerating 3D single-molecule localization microscopy using blind sparse inpainting. Journal of Biomedical Optics. 26
Liang D, Cheng J, Ke Z, et al. (2020) Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks. Ieee Signal Processing Magazine. 37: 141-151
Ke Z, Cheng J, Ying L, et al. (2020) An unsupervised deep learning method for multi-coil cine MRI. Physics in Medicine and Biology. 65: 235041
Subhas N, Li H, Yang M, et al. (2020) Diagnostic interchangeability of deep convolutional neural networks reconstructed knee MR images: preliminary experience. Quantitative Imaging in Medicine and Surgery. 10: 1748-1762
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