Aydogan Özcan

36.6k total citations · 16 hit papers
463 papers, 25.2k citations indexed

About

Aydogan Özcan is a scholar working on Biomedical Engineering, Atomic and Molecular Physics, and Optics and Biophysics. According to data from OpenAlex, Aydogan Özcan has authored 463 papers receiving a total of 25.2k indexed citations (citations by other indexed papers that have themselves been cited), including 221 papers in Biomedical Engineering, 187 papers in Atomic and Molecular Physics, and Optics and 147 papers in Biophysics. Recurrent topics in Aydogan Özcan's work include Digital Holography and Microscopy (152 papers), Advanced Fluorescence Microscopy Techniques (93 papers) and Biosensors and Analytical Detection (84 papers). Aydogan Özcan is often cited by papers focused on Digital Holography and Microscopy (152 papers), Advanced Fluorescence Microscopy Techniques (93 papers) and Biosensors and Analytical Detection (84 papers). Aydogan Özcan collaborates with scholars based in United States, Russia and Türkiye. Aydogan Özcan's co-authors include Yair Rivenson, Derek Tseng, Ting‐Wei Su, Ahmet F. Coskun, Yibo Zhang, Onur Mudanyali, Yi Luo, Alon Greenbaum, Mona Jarrahi and Yichen Wu and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Aydogan Özcan

437 papers receiving 24.2k citations

Hit Papers

All-optical machine learning using diffractive de... 2012 2026 2016 2021 2018 2017 2020 2019 2018 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. All-optical machine learning using diffractive deep neural networks
    2018 1.4k citations Xing Lin, Yair Rivenson et al. profile →
  2. Phase recovery and holographic image reconstruction using deep learning in neural networks
    2017 703 citations Yair Rivenson, Yibo Zhang et al. Light Science & Applications profile →
  3. Inference in artificial intelligence with deep optics and photonics
    2020 612 citations Aydogan Özcan et al. profile →
  4. On the use of deep learning for computational imaging
    2019 545 citations Aydogan Özcan et al. profile →
  5. Deep learning enables cross-modality super-resolution in fluorescence microscopy
    2018 539 citations Hongda Wang, Yair Rivenson et al. profile →
  6. Emerging Technologies for Next-Generation Point-of-Care Testing
    2015 528 citations Aydogan Özcan et al. profile →
  7. Deep learning microscopy
    2017 448 citations Yair Rivenson, Zoltán Göröcs et al. profile →
  8. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
    2019 406 citations Yair Rivenson, Hongda Wang et al. profile →
  9. Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy
    2012 401 citations Wei Luo, Zoltán Göröcs et al. profile →
  10. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
    2019 255 citations Yair Rivenson, Yibo Zhang et al. Light Science & Applications profile →
  11. Deep learning-based transformation of H&E stained tissues into special stains
    2021 185 citations Kevin de Haan, Yair Rivenson et al. profile →
  12. Deep learning-enabled virtual histological staining of biological samples
    2023 150 citations Bijie Bai, Xilin Yang et al. Light Science & Applications profile →
  13. Computational imaging without a computer: seeing through random diffusers at the speed of light
    2022 125 citations Yi Luo, Yifan Zhao et al. arXiv (Cornell University) profile →
  14. High-throughput terahertz imaging: progress and challenges
    2023 105 citations Jingxi Li, Yuhang Li et al. Light Science & Applications profile →
  15. Diffractive optical computing in free space
    2024 83 citations Jingtian Hu, Deniz Mengü et al. profile →
  16. Machine learning in point-of-care testing: innovations, challenges, and opportunities
    2025 47 citations Merve Eryılmaz, Dino Di Carlo et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Aydogan Özcan United States 84 11.8k 7.7k 5.2k 4.9k 4.7k 463 25.2k
Michael S. Feld United States 92 15.0k 1.3× 12.0k 1.6× 2.7k 0.5× 11.6k 2.4× 5.5k 1.2× 436 36.8k
Ramachandra R. Dasari United States 72 12.4k 1.1× 7.3k 0.9× 1.9k 0.4× 8.6k 1.7× 5.3k 1.1× 228 25.7k
YongKeun Park South Korea 63 5.0k 0.4× 7.2k 0.9× 980 0.2× 3.0k 0.6× 1.4k 0.3× 324 13.3k
Qian Chen China 72 2.7k 0.2× 4.0k 0.5× 4.3k 0.8× 859 0.2× 798 0.2× 1.3k 23.6k
Ge Wang United States 74 14.0k 1.2× 614 0.1× 1.2k 0.2× 899 0.2× 1.4k 0.3× 1.0k 28.6k
Demetri Psaltis United States 70 5.8k 0.5× 8.5k 1.1× 9.4k 1.8× 1.1k 0.2× 411 0.1× 575 19.6k
Xiaowei Zhuang United States 95 8.8k 0.8× 4.7k 0.6× 2.3k 0.4× 15.5k 3.1× 21.2k 4.5× 196 41.2k
Colin J. R. Sheppard Australia 61 8.2k 0.7× 7.0k 0.9× 2.0k 0.4× 5.2k 1.1× 722 0.2× 507 14.5k
Gabriel Popescu United States 54 5.7k 0.5× 9.3k 1.2× 654 0.1× 4.2k 0.8× 750 0.2× 252 12.7k
Kishan Dholakia United Kingdom 80 15.5k 1.3× 19.9k 2.6× 5.2k 1.0× 2.5k 0.5× 1.5k 0.3× 468 27.6k

Countries citing papers authored by Aydogan Özcan

Since Specialization
Citations

This map shows the geographic impact of Aydogan Özcan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aydogan Özcan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aydogan Özcan more than expected).

Fields of papers citing papers by Aydogan Özcan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aydogan Özcan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aydogan Özcan. The network helps show where Aydogan Özcan may publish in the future.

Co-authorship network of co-authors of Aydogan Özcan

This figure shows the co-authorship network connecting the top 25 collaborators of Aydogan Özcan. A scholar is included among the top collaborators of Aydogan Özcan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aydogan Özcan. Aydogan Özcan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Yijie, Luzhe Huang, Nir Pillar, et al.. (2025). Virtual staining of label-free tissue in imaging mass spectrometry. Science Advances. 11(31). eadv0741–eadv0741.
2.
Koydemir, Hatice Ceylan, Merve Eryılmaz, Kevin de Haan, et al.. (2025). Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning. Science Advances. 11(2). eads2757–eads2757. 1 indexed citations
3.
Mabbott, Samuel, et al.. (2024). Advances in point-of-care optical biosensing for underserved populations. TrAC Trends in Analytical Chemistry. 175. 117731–117731. 11 indexed citations
4.
Gan, Tianyi, Jingxi Li, Deniz Mengü, et al.. (2024). All-optical image denoising using a diffractive visual processor. Light Science & Applications. 13(1). 43–43. 25 indexed citations
5.
Li, Yuhang, Jingxi Li, & Aydogan Özcan. (2024). Nonlinear encoding in diffractive information processing using linear optical materials. Light Science & Applications. 13(1). 173–173. 19 indexed citations
6.
Özcan, Aydogan, et al.. (2023). Time‐Lapse Image Classification Using a Diffractive Neural Network. SHILAP Revista de lepidopterología. 5(5). 16 indexed citations
7.
Li, Yuhang, Jingxi Li, Yifan Zhao, et al.. (2023). Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning‐Designed Diffractive Polarization Transformer. Advanced Materials. 35(51). e2303395–e2303395. 17 indexed citations
8.
Yang, Xilin, Derek Tseng, Yi Luo, et al.. (2023). Amphiphilic Particle-Stabilized Nanoliter Droplet Reactors with a Multimodal Portable Reader for Distributive Biomarker Quantification. ACS Nano. 17(20). 19952–19960. 12 indexed citations
9.
Bai, Bijie, Yi Luo, Tianyi Gan, et al.. (2022). To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects. arXiv (Cornell University). 2(1). 62 indexed citations
10.
Mengü, Deniz, et al.. (2022). At the intersection of optics and deep learning: statistical inference, computing, and inverse design. Advances in Optics and Photonics. 14(2). 209–209. 39 indexed citations
11.
Brown, Calvin, et al.. (2021). Neural Network-Based On-Chip Spectroscopy Using a Scalable Plasmonic Encoder. ACS Nano. 15(4). 6305–6315. 62 indexed citations
12.
13.
Ballard, Zachary S., et al.. (2020). Contact lens-based lysozyme detection in tear using a mobile sensor. Lab on a Chip. 20(8). 1493–1502. 34 indexed citations
14.
Snow, Jonathan W., et al.. (2019). Rapid imaging, detection, and quantification of Nosema ceranae spores in honey bees using mobile phone-based fluorescence microscopy. Lab on a Chip. 19(5). 789–797. 35 indexed citations
15.
Wu, Yichen, Yilin Luo, Gunvant Chaudhari, et al.. (2019). Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light Science & Applications. 8(1). 25–25. 98 indexed citations
16.
Wu, Yichen, Aniruddha Ray, Qingshan Wei, et al.. (2019). Particle-Aggregation Based Virus Sensor Using Deep Learning and Lensless Digital Holography. Conference on Lasers and Electro-Optics. 1 indexed citations
17.
Wu, Yichen, Yi Luo, Cheng Chen, et al.. (2018). Label-Free Bioaerosol Sensing Using Mobile Microscopy and Deep Learning. ACS Photonics. 5(11). 4617–4627. 64 indexed citations
18.
Göröcs, Zoltán, Miu Tamamitsu, Vittorio Bianco, et al.. (2018). A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples. Light Science & Applications. 7(1). 66–66. 131 indexed citations
19.
Wei, Qingshan, Guillermo P. Acuna, Carolin Vietz, et al.. (2017). Plasmonics Enhanced Smartphone Fluorescence Microscopy. Scientific Reports. 7(1). 2124–2124. 62 indexed citations
20.
Özcan, Aydogan, Michel J. F. Digonnet, & G. S. Kino. (2004). Simplified inverse Fourier transform technique to determine second-order optical nonlinearity profiles using a reference sample. Optical Fiber Communication Conference. 2.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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