Mo Deng, Ph.D.
Affiliations: | 2011 | University of Illinois at Chicago, Chicago, IL, United States |
Area:
Applied Mathematics, Bioinformatics BiologyGoogle:
"Mo Deng"Parents
Sign in to add mentor| George Barbastathis | grad student | MIT (E-Tree) | ||
| Stephen S. Yau | grad student | 2011 | University of Illinois, Chicago | |
| (Natural Vector Method of Characterizing, Clustering and Phylogeny of DNA, Genome and Proteins.) | ||||
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Publications
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| Deng M, Li S, Zhang Z, et al. (2020) On the interplay between physical and content priors in deep learning for computational imaging. Optics Express. 28: 24152-24170 |
| Deng M, Li S, Goy A, et al. (2020) Learning to synthesize: robust phase retrieval at low photon counts. Light, Science & Applications. 9: 36 |
| Deng M, Goy A, Li S, et al. (2020) Probing shallower: perceptual loss trained Phase Extraction Neural Network (PLT-PhENN) for artifact-free reconstruction at low photon budget. Optics Express. 28: 2511-2535 |
| Li S, Deng M, Lee J, et al. (2018) Imaging through glass diffusers using densely connected convolutional networks Optica. 5: 803 |
| Yu C, Deng M, Zheng L, et al. (2014) DFA7, a new method to distinguish between intron-containing and intronless genes. Plos One. 9: e101363 |
| Yu C, Deng M, Cheng SY, et al. (2013) Protein space: a natural method for realizing the nature of protein universe. Journal of Theoretical Biology. 318: 197-204 |
| Deng M, Yu C, Liang Q, et al. (2011) A novel method of characterizing genetic sequences: genome space with biological distance and applications. Plos One. 6: e17293 |