Yair Rivenson

11.6k total citations · 8 hit papers
124 papers, 7.4k citations indexed

About

Yair Rivenson is a scholar working on Atomic and Molecular Physics, and Optics, Biophysics and Biomedical Engineering. According to data from OpenAlex, Yair Rivenson has authored 124 papers receiving a total of 7.4k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Atomic and Molecular Physics, and Optics, 44 papers in Biophysics and 36 papers in Biomedical Engineering. Recurrent topics in Yair Rivenson's work include Digital Holography and Microscopy (52 papers), Cell Image Analysis Techniques (30 papers) and Sparse and Compressive Sensing Techniques (26 papers). Yair Rivenson is often cited by papers focused on Digital Holography and Microscopy (52 papers), Cell Image Analysis Techniques (30 papers) and Sparse and Compressive Sensing Techniques (26 papers). Yair Rivenson collaborates with scholars based in United States, Israel and Russia. Yair Rivenson's co-authors include Aydogan Özcan, Yi Luo, Yibo Zhang, Adrian Stern, Harun Günaydın, Mona Jarrahi, Nezih Tolga Yardimci, Muhammed Veli, Hongda Wang and Xing Lin and has published in prestigious journals such as Science, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Yair Rivenson

119 papers receiving 7.0k citations

Hit Papers

All-optical machine learning using diffractive deep neura... 2017 2026 2020 2023 2018 2017 2018 2017 2019 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. Deep learning enables cross-modality super-resolution in fluorescence microscopy
    2018 539 citations Hongda Wang, Yair Rivenson et al. profile →
  4. Deep learning microscopy
    2017 448 citations Yair Rivenson, Zoltán Göröcs et al. Optica profile →
  5. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
    2019 406 citations Yair Rivenson, Hongda Wang et al. profile →
  6. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
    2019 255 citations Yair Rivenson, Tairan Liu et al. Light Science & Applications profile →
  7. Deep learning-based transformation of H&E stained tissues into special stains
    2021 185 citations Kevin de Haan, Yijie Zhang et al. Nature Communications profile →
  8. Computational imaging without a computer: seeing through random diffusers at the speed of light
    2022 125 citations Yi Luo, Jingxi Li 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
Yair Rivenson United States 36 2.4k 2.4k 2.2k 1.8k 1.6k 124 7.4k
Guohai Situ China 37 896 0.4× 2.3k 1.0× 650 0.3× 1.2k 0.6× 1.6k 1.0× 143 6.0k
David J. Brady United States 55 599 0.2× 2.9k 1.2× 3.4k 1.6× 5.3k 2.9× 2.2k 1.4× 421 12.5k
Edmund Y. Lam Hong Kong 40 393 0.2× 1.7k 0.7× 1.5k 0.7× 1.4k 0.8× 2.2k 1.4× 432 6.8k
Sylvain Gigan France 41 2.6k 1.1× 5.1k 2.1× 3.4k 1.6× 2.7k 1.5× 1.1k 0.7× 149 9.8k
Zeev Zalevsky Israel 41 289 0.1× 3.2k 1.3× 1.7k 0.8× 2.6k 1.4× 1.5k 0.9× 635 8.3k
Gerald S. Buller United Kingdom 47 1.6k 0.7× 2.8k 1.1× 1.8k 0.8× 1.5k 0.8× 263 0.2× 257 7.6k
Changhuei Yang United States 45 397 0.2× 4.7k 2.0× 1.7k 0.8× 3.8k 2.1× 1.5k 0.9× 177 9.5k
George Barbastathis United States 43 241 0.1× 4.0k 1.7× 1.9k 0.9× 2.4k 1.3× 1.3k 0.8× 307 7.6k
Graham M. Gibson United Kingdom 38 634 0.3× 4.7k 2.0× 1.7k 0.8× 3.5k 1.9× 1.1k 0.7× 141 8.1k
Jonathan Leach United Kingdom 50 2.5k 1.0× 7.2k 3.0× 1.6k 0.7× 3.5k 1.9× 379 0.2× 181 9.2k

Countries citing papers authored by Yair Rivenson

Since Specialization
Citations

This map shows the geographic impact of Yair Rivenson'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 Yair Rivenson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yair Rivenson more than expected).

Fields of papers citing papers by Yair Rivenson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yair Rivenson. 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 Yair Rivenson. The network helps show where Yair Rivenson may publish in the future.

Co-authorship network of co-authors of Yair Rivenson

This figure shows the co-authorship network connecting the top 25 collaborators of Yair Rivenson. A scholar is included among the top collaborators of Yair Rivenson 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 Yair Rivenson. Yair Rivenson 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.
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
2.
Rivenson, Yair, et al.. (2023). Building a nonclinical pathology laboratory of the future for pharmaceutical research excellence. Drug Discovery Today. 28(10). 103747–103747. 6 indexed citations
3.
Bai, Bijie, Hongda Wang, Yuzhu Li, et al.. (2022). Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning. SHILAP Revista de lepidopterología. 2022. 9786242–9786242. 39 indexed citations
4.
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
5.
Mengü, Deniz, Yifan Zhao, Nezih Tolga Yardimci, et al.. (2021). Misalignment Insensitive Diffractive Optical Networks. Bulletin of the American Physical Society. 1 indexed citations
6.
Rivenson, Yair, Ashley E. Prosper, Kevin de Haan, et al.. (2021). Automatic segmentation of peripheral arteries and veins in ferumoxytol‐enhanced MR angiography. Magnetic Resonance in Medicine. 87(2). 984–998. 5 indexed citations
7.
Li, Jingxi, Xiaoran Zhang, Di Wu, et al.. (2021). Biopsy-free in vivo virtual histology of skin using deep learning. Light Science & Applications. 10(1). 233–233. 64 indexed citations
8.
Li, Jingxi, Deniz Mengü, Nezih Tolga Yardimci, et al.. (2021). Spectrally encoded single-pixel machine vision using diffractive networks. Science Advances. 7(13). 138 indexed citations
9.
Haan, Kevin de, Yijie Zhang, Jonathan E. Zuckerman, et al.. (2021). Deep learning-based transformation of H&E stained tissues into special stains. Nature Communications. 12(1). 4884–4884. 185 indexed citations breakdown →
10.
Rivenson, Yair, Kevin de Haan, William D. Wallace, & Aydogan Özcan. (2020). Emerging Advances to Transform Histopathology Using Virtual Staining. SHILAP Revista de lepidopterología. 2020. 9647163–9647163. 64 indexed citations
11.
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
12.
Rivenson, Yair, Yichen Wu, & Aydogan Özcan. (2019). Deep learning in holography and coherent imaging. Light Science & Applications. 8(1). 85–85. 198 indexed citations
13.
Wu, Yichen, Yi Luo, Cheng Chen, et al.. (2019). Label-free Bio-aerosol Sensing Using On-Chip Holographic Microscopy and Deep Learning. Conference on Lasers and Electro-Optics. 2 indexed citations
14.
Rivenson, Yair, Tairan Liu, Zhensong Wei, et al.. (2019). PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Science & Applications. 8(1). 23–23. 255 indexed citations breakdown →
15.
Bai, Bijie, Hongda Wang, Tairan Liu, et al.. (2019). Pathological crystal imaging with single‐shot computational polarized light microscopy. Journal of Biophotonics. 13(1). e201960036–e201960036. 20 indexed citations
16.
Luo, Yi, Deniz Mengü, Nezih Tolga Yardimci, et al.. (2019). Design of task-specific optical systems using broadband diffractive neural networks. Light Science & Applications. 8(1). 112–112. 188 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.
Rivenson, Yair, Zoltán Göröcs, Harun Günaydın, et al.. (2017). Deep learning microscopy. Optica. 4(11). 1437–1437. 448 indexed citations breakdown →
20.
Rivenson, Yair, Adrian Stern, & Joseph Rosen. (2011). Compressive multiple view projection incoherent holography. Optics Express. 19(7). 6109–6109. 49 indexed citations

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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