World's Best Scientists 2026 revealed!
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Engineering and Technology
USA
2026

D-Index & Metrics

Engineering and Technology

D-Index
98
Citations
35930
World Ranking
162
National Ranking
59

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in United States Leader Award
  • 2025 - Research.com Engineering and Technology in United States Leader Award

Overview

Aydogan Ozcan is affiliated with the University of California, Los Angeles in the United States and has contributed extensively to the field of engineering, with a specialization in electrical and electronic engineering, biomedical engineering, and artificial intelligence. Their research portfolio encompasses various subfields including biophysics and atomic and molecular physics, as well as optics.

The scientist's work spans several key topics, such as:

  • Neural Networks and Reservoir Computing
  • Photonic and Optical Devices
  • Digital Holography and Microscopy
  • Cell Image Analysis Techniques
  • Image Processing Techniques and Applications
  • Optical Network Technologies
  • Biosensors and Analytical Detection

Among recent papers authored by Aydogan Ozcan are:

  • Inference in artificial intelligence with deep optics and photonics, 2020, Nature
  • Deep learning-based transformation of H&E stained tissues into special stains, 2021, Nature Communications
  • Deep learning-enabled virtual histological staining of biological samples, 2023, Light Science & Applications
  • Lateral flow test engineering and lessons learned from COVID-19, 2023, Nature Reviews Bioengineering
  • Machine learning and computation-enabled intelligent sensor design, 2021, Nature Machine Intelligence

Aydogan Ozcan frequently collaborates with the following co-authors:

  • Bijie Bai
  • Yair Rivenson
  • Mona Jarrahi
  • Jingxi Li
  • Deniz Mengü

The scientist has published regularly in prominent venues, including:

  • arXiv (Cornell University)
  • Conference on Lasers and Electro-Optics
  • Light Science & Applications
  • Nature Communications
  • Science Advances

In addition to research articles, Aydogan Ozcan has contributed to the academic literature with a book published through Springer Science+Business Media titled Computing and Data Science in 2021.

Best Publications

  • All-optical machine learning using diffractive deep neural networks

    Xing Lin;Yair Rivenson;Nezih T. Yardimci;Muhammed Veli

  • Inference in artificial intelligence with deep optics and photonics.

    Gordon Wetzstein;Aydogan Ozcan;Sylvain Gigan;Shanhui Fan

  • On the use of deep learning for computational imaging

    George Barbastathis;Aydogan Ozcan;Guohai Situ

  • Deep learning enables cross-modality super-resolution in fluorescence microscopy

    Hongda Wang;Yair Rivenson;Yiyin Jin;Zhensong Wei

  • Emerging Technologies for Next-Generation Point-of-Care Testing

    Sandeep Kumar Vashist;Peter B. Luppa;Leslie Y. Yeo;Aydogan Ozcan

  • Phase recovery and holographic image reconstruction using deep learning in neural networks

    Yair Rivenson;Yibo Zhang;Harun Günaydın;Da Teng

  • Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy

    Alon Greenbaum;Wei Luo;Ting-Wei Su;Zoltán Göröcs

  • Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.

    Yair Rivenson;Hongda Wang;Zhensong Wei;Kevin de Haan

  • Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications

    Onur Mudanyali;Derek Tseng;Chulwoo Oh;Serhan O. Isikman

  • Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution

    Waheb Bishara;Ting-Wei Su;Ahmet F. Coskun;Aydogan Ozcan

  • Phase recovery and holographic image reconstruction using deep learning in neural networks

    Yair Rivenson;Yibo Zhang;Harun Gunaydin;Da Teng

  • All-Optical Machine Learning Using Diffractive Deep Neural Networks

    Xing Lin;Yair Rivenson;Nezih T. Yardimci;Muhammed Veli

  • Optofluidic Fluorescent Imaging Cytometry on a Cell Phone

    Hongying Zhu;Sam Mavandadi;Ahmet F. Coskun;Oguzhan Yaglidere

  • Detection and spatial mapping of mercury contamination in water samples using a smart-phone.

    Qingshan Wei;Richie Nagi;Kayvon Sadeghi;Steve Feng

  • Optical imaging techniques for point-of-care diagnostics

    Hongying Zhu;Serhan O. Isikman;Onur Mudanyali;Alon Greenbaum

  • PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning.

    Yair Rivenson;Tairan Liu;Zhensong Wei;Yibo Zhang

  • Handheld high-throughput plasmonic biosensor using computational on-chip imaging

    Arif E Cetin;Ahmet F Coskun;Ahmet F Coskun;Betty C Galarreta;Betty C Galarreta;Min Huang

  • Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools

    Aydogan Ozcan

  • Lensless Imaging and Sensing

    Aydogan Ozcan;Euan McLeod

  • Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays.

    Brandon Berg;Bingen Cortazar;Derek Tseng;Haydar Ozkan

  • Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array.

    Waheb Bishara;Uzair Sikora;Onur Mudanyali;Ting Wei Su

  • Lensfree holographic imaging for on-chip cytometry and diagnostics.

    Sungkyu Seo;Ting-Wei Su;Derek K. Tseng;Anthony Erlinger

  • Quantum dot enabled detection of Escherichia coli using a cell-phone.

    Hongying Zhu;Uzair Sikora;Aydogan Ozcan

Frequent Co-Authors

Michel J. F. Digonnet
Michel J. F. Digonnet Stanford University
Gordon S. Kino
Gordon S. Kino Stanford University
Dino Di Carlo
Dino Di Carlo University of California, Los Angeles
Brett E. Bouma
Brett E. Bouma Harvard University
Hatice Altug
Hatice Altug École Polytechnique Fédérale de Lausanne
Ali Khademhosseini
Ali Khademhosseini Terasaki Foundation
Mona Jarrahi
Mona Jarrahi University of California, Los Angeles
Utkan Demirci
Utkan Demirci Stanford University
Paul S. Weiss
Paul S. Weiss University of California, Los Angeles
Philip Tinnefeld
Philip Tinnefeld Ludwig-Maximilians-Universität München

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