Ziling Wu
Ziling Wu
Postdoctoral researcher, MIT
Verified email at
Cited by
Cited by
Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning
Y Zhu, Z Wu, WD Hartley, JM Sietins, CB Williams, ZY Hang
Additive Manufacturing 34, 101183, 2020
Quantitative 3D structural analysis of the cellular microstructure of sea urchin spines (I): Methodology
T Yang, Z Wu, H Chen, Y Zhu, L Li
Acta Biomaterialia 107, 204-217, 2020
Heterogeneous materials design in additive manufacturing: Model calibration and uncertainty-guided model selection
D Garcia, Z Wu, JY Kim, ZY Hang, Y Zhu
Additive Manufacturing 27, 61-71, 2019
High strength and damage-tolerance in echinoderm stereom as a natural bicontinuous ceramic cellular solid
T Yang, Z Jia, Z Wu, H Chen, Z Deng, L Chen, Y Zhu, L Li
Nature Communications 13 (1), 6083, 2022
Single-frame label-free cell tomography at speed of more than 10,000 volumes per second
B Ge, Y He, M Deng, MH Rahman, Y Wang, Z Wu, CHN Wong, MK Chan, ...
arXiv preprint arXiv:2202.03627, 2022
Quantitative 3D structural analysis of the cellular microstructure of sea urchin spines (II): Large-volume structural analysis
H Chen, T Yang, Z Wu, Z Deng, Y Zhu, L Li
Acta Biomaterialia 107, 218-231, 2020
Robust X-ray sparse-view phase tomography via hierarchical synthesis convolutional neural networks
Z Wu, A Alorf, T Yang, L Li, Y Zhu
arXiv preprint arXiv:1901.10644, 2019
Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID)
Z Wu, I Kang, Y Yao, Y Jiang, J Deng, J Klug, S Vogt, G Barbastathis
eLight 3 (1), 7, 2023
Physics-informed neural network for phase imaging based on transport of intensity equation
X Wu, Z Wu, SC Shanmugavel, ZY Hang, Y Zhu
Optics Express 30 (24), 43398-43416, 2022
Automatic crack detection and analysis for biological cellular materials in X-ray in situ tomography measurements
Z Wu, T Yang, Z Deng, B Huang, H Liu, Y Wang, Y Chen, MC Stoddard, ...
Integrating Materials and Manufacturing Innovation 8, 559-569, 2019
Structured illumination-based phase retrieval via Generative Adversarial Network
Z Wu, X Wu, Y Zhu
Quantitative Phase Imaging VI 11249, 14-22, 2020
Hierarchical convolutional network for sparse-view X-ray CT reconstruction
Z Wu, T Yang, L Li, Y Zhu
Computational Imaging IV 10990, 141-146, 2019
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
I Kang, Z Wu, Y Jiang, Y Yao, J Deng, J Klug, S Vogt, G Barbastathis
Light: Science & Applications 12 (1), 131, 2023
Deep learning-based low-dose tomography reconstruction with hybrid-dose measurements
Z Wu, T Bicer, Z Liu, V De Andrade, Y Zhu, IT Foster
2020 IEEE/ACM Workshop on Machine Learning in High Performance Computing …, 2020
Feature-based sparse angle tomography reconstruction for dynamic characterization of bio-cellular materials
Z Wu, T Yang, L Li, Y Zhu
Computational Imaging III 10669, 90-97, 2018
Learning a model-based neural network for quantitative phase imaging based on the transport of intensity
X Wu, Z Wu, Y Zhu
Computational Imaging V 11396, 16-24, 2020
Superresolution phase retrieval from non-sinusoidal structure illumination
Z Wu, Y Zhu
Imaging Systems and Applications, JTu5A. 21, 2017
Texture orientation-resolving imaging with structure illumination
Z Wu, X Li, Y Zhu
Computational Imaging II 10222, 116-121, 2017
On the use of deep learning for three-dimensional computational imaging
G Barbastathis, S Pang, I Kang, Z Wu, Z Liu, Z Guo, F Zhang
Practical Holography XXXVII: Displays, Materials, and Applications 12445 …, 2023
Photon-starved x-ray ptychographic imaging using spatial pyramid atrous convolution end-to-end reconstruction (ptychospacer)
Z Wu, I Kang, T Zhou, V Coykendall, B Ge, MJ Cherukara, G Barbastathis
Computational Optical Sensing and Imaging, CF1D. 6, 2022
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