Light People: Professor Aydogan Ozcan

Tingting Sun

doi:10.1038/s41377-021-00643-1

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Light People: Professor Aydogan Ozcan

Light Peoples

- Light People: Professor Aydogan Ozcan
- Light People: Professor Aydogan Ozcan
- LSA 2021年10卷第11期页码：2004-2014
- Affiliations：
  
  Light Publishing Group, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 3888 Dong Nan Hu Road, Changchun 130033, China
- Author bio：
  
  Tingting Sun (suntt@ciomp.ac.cn)
- Funds：
- DOI：10.1038/s41377-021-00643-1
  中图分类号：
- 纸质出版日期：2021-11-30，
  
  网络出版日期：2021-10-05，
- Accepted：
Scan QR Code
Light People: Professor Aydogan Ozcan[J]. LSA, 2021,10(11):2004-2014.

Sun, T. T. Light People: Professor Aydogan Ozcan. Light: Science & Applications, 10, 2004-2014 (2021).
Light People: Professor Aydogan Ozcan[J]. LSA, 2021,10(11):2004-2014. DOI： 10.1038/s41377-021-00643-1.

Sun, T. T. Light People: Professor Aydogan Ozcan. Light: Science & Applications, 10, 2004-2014 (2021). DOI： 10.1038/s41377-021-00643-1.

摘要

Abstract

In 2016

the news that Google's artificial intelligence (AI) robot AlphaGo

based on the principle of deep learning

won the victory over lee Sedol

the former world Go champion and the famous 9th Dan competitor of Korea

caused a sensation in both fields of AI and Go

which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present

deep learning has penetrated into our daily life

such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California

Los Angeles (UCLA) led his team to research deep learning algorithms

which provided new ideas for the exploring of optical computational imaging and sensing technology

and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan

Fellow of the National Academy of Inventors and Chancellor's Professor of UCLA

to unscramble his latest scientific research results and foresight for the future development of related fields

and to share his journey of pursuing Optics

his indissoluble relationship with Light: Science & Applications (LSA)

and his experience in talent cultivation.

关键词

Keywords

references

Zhang, Y. J. et al. Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data.Light Sci. Appl.10, 155,https://doi.org/10.1038/s41377-021-00594-7https://doi.org/10.1038/s41377-021-00594-7(2021)..

Rivenson, Y. et al. Phase recovery and holographic image reconstruction using deep learning in neural networks.Light Sci. Appl.7, 17141,https://doi.org/10.1038/lsa.2017.141https://doi.org/10.1038/lsa.2017.141(2018)..

Wu, Y. C. et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery.Optica5, 704–710,https://doi.org/10.1364/OPTICA.5.000704https://doi.org/10.1364/OPTICA.5.000704(2018)..

Wu, Y. C. et al. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram.Light Sci. Appl.8, 25,https://doi.org/10.1038/s41377-019-0139-9https://doi.org/10.1038/s41377-019-0139-9(2019)..

Lin, X. et al. All-optical machine learning using diffractive deep neural networks.Science361, 1004–1008,https://doi.org/10.1126/science.aat8084https://doi.org/10.1126/science.aat8084(2018)..

Rahman, S. S. et al. Ensemble learning of diffractive optical networks.Light Sci. Appl.10, 14,https://doi.org/10.1038/s41377-020-00446-whttps://doi.org/10.1038/s41377-020-00446-w(2021)..

Veli, M. et al. Terahertz pulse shaping using diffractive surfaces.Nat. Commun.12, 37,https://doi.org/10.1038/s41467-020-20268-zhttps://doi.org/10.1038/s41467-020-20268-z(2021)..

Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks.Light Sci. Appl.8, 112,https://doi.org/10.1038/s41377-019-0223-1https://doi.org/10.1038/s41377-019-0223-1(2019)..

Kulce, O. et al. All-optical information-processing capacity of diffractive surfaces.Light Sci. Appl.10, 25,https://doi.org/10.1038/s41377-020-00439-9https://doi.org/10.1038/s41377-020-00439-9(2021)..

Li, J. X. et al. Spectrally encoded single-pixel machine vision using diffractive networks.Sci. Adv.7, 13,https://doi.org/10.1126/sciadv.abd7690https://doi.org/10.1126/sciadv.abd7690(2021)..

Kulce, O. et al. All-optical synthesis of an arbitrary linear transformation using diffractive surfaces.Light Sci. Appl.10, 196,https://doi.org/10.1038/s41377-021-00623-5https://doi.org/10.1038/s41377-021-00623-5(2021)..

Rivenson, Y. et al. Deep learning microscopy.Optica4, 1437–1443,https://doi.org/10.1364/OPTICA.4.001437https://doi.org/10.1364/OPTICA.4.001437(2017)..

Wang, H. D. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy.Nat. Methods16, 103–110,https://doi.org/10.1038/s41592-018-0239-0https://doi.org/10.1038/s41592-018-0239-0(2019)..

Wu, Y. C. et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning.Nat. Methods16, 1323–1331,https://doi.org/10.1038/s41592-019-0622-5https://doi.org/10.1038/s41592-019-0622-5(2019)..

Rivenson, Y. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.Nat. Biomed. Eng.3, 466–477,https://doi.org/10.1038/s41551-019-0362-yhttps://doi.org/10.1038/s41551-019-0362-y(2019)..

Zhang, Y. J. et al. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue.Light Sci. Appl.9, 78,https://doi.org/10.1038/s41377-020-0315-yhttps://doi.org/10.1038/s41377-020-0315-y(2020)..

de Haan, K. et al. Deep learning-based transformation of H&E stained tissues into special stains.Nat. Commun.12, 4884,https://doi.org/10.1038/s41467-021-25221-2https://doi.org/10.1038/s41467-021-25221-2(2021)..

Gӧrӧcs, Z. et al. A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples.Light Sci. Appl.7, 66,https://doi.org/10.1038/s41377-018-0067-0https://doi.org/10.1038/s41377-018-0067-0(2018)..

Joung, H. A. et al. Point-of-care serodiagnostic test for early-stage Lyme disease using a multiplexed paper-based immunoassay and machine learning.ACS Nano14, 229–240,https://doi.org/10.1021/acsnano.9b08151https://doi.org/10.1021/acsnano.9b08151(2020)..

Ballard, Z. S. et al. Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors.npj Digital Med.3, 66,https://doi.org/10.1038/s41746-020-0274-yhttps://doi.org/10.1038/s41746-020-0274-y(2020)..

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