Yanxin Luo

About

Yanxin Luo is a faculty member at the College of Education at the University of Arizona. His professional focus is in higher education, and he is involved in various academic units including Disability & Psychoeducational Studies, Educational Policy Studies & Practice, Educational Psychology, and Teaching, Learning & Sociocultural Studies. He is engaged in research and teaching related to these fields, contributing to the college's mission of advancing education through scholarly work and community engagement. His contact information is provided as part of the faculty and staff directory, indicating his active role within the college community.

Research topics

• Optics
• Materials science
• Computer science
• Physics
• Artificial intelligence

Selected publications

• Varifocal Metalens for Optical Sectioning Fluorescence Microscopy

  Nano Letters · 2021-06-07 · 223 citations

  articleOpen access1st authorCorresponding

  Fluorescence microscopy with optical sectioning capabilities is extensively utilized in biological research to obtain three-dimensional structural images of volumetric samples. Tunable lenses have been applied in microscopy for axial scanning to acquire multiplane images. However, images acquired by conventional tunable lenses suffer from spherical aberration and distortions. Here, we design, fabricate, and implement a dielectric Moiré metalens for fluorescence imaging. The Moiré metalens consis…

  PublisherOA PDFDOI

• Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences

  arXiv (Cornell University) · 2022-01-27 · 68 citations

  preprintOpen accessSenior author

  Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely ClinicalBERT, also achieved state-of-the-art results when performed on clinical named entity recognition and natural language inference tasks. One of the core limitations of these transformers is the substantial memory consumption due to their full self-attention mechanism. To overcome this, long sequence transformer mode…

  PublisherOA PDFDOI

• Metasurface‐Based Abrupt Autofocusing Beam for Biomedical Applications

  Small Methods · 2022-02-24 · 54 citations

  articleOpen access1st authorCorresponding

  Manipulation and precise delivery of optical energies in the regions of interest within specimens require different strategies. Hence, proper control of input beam parameters is a prerequisite. One of the prominent methods is metasurface optics, capable of crafting properties of light at nanoscales. Here, the generation of an abrupt autofocusing (AAF) beam by a nanophotonic metasurface for biomedical applications is demonstrated. Fluorescence guided laser microprofiling of mouse cardiac samples…

  PublisherOA PDFDOI

• Retaining spatial resolution multifocal confocal fluorescence microscopy with deep learning

  Optics Express · 2025-02-24 · 6 citations

  articleOpen accessSenior author

  Confocal microscopy is a standard modality for volumetric imaging of biological samples due to its high spatial resolution and signal-to-noise ratio (SNR). However, the slow point-by-point scanning process limits its image acquisition speed. Multifocal illumination allows for faster acquisition but compromises spatial resolution. Here, we introduce a deep learning approach for multifocal confocal microscopy that achieves faster acquisition while preserving high resolution. The proposed model is…

  PublisherDOI

• Radial-balanced phase transfer functions for accurate retrieval in quantitative differential phase contrast microscopy

  Optics Express · 2025-06-16 · 1 citations

  articleOpen accessSenior author

  Quantitative differential phase contrast microscopy (qDPC) is a potent quantitative phase imaging (QPI) technique for biological applications due to its high resolution and rapid image acquisition. Traditionally, qDPC employs an asymmetric 12 axis illumination pattern and reconstructs the phase using Tikhonov regularization. Numerous endeavors have been made to enhance qDPC performance, including reducing the number of illumination patterns and refining retrieval algorithms. However, a comprehen…

  PublisherDOI

Frequent coauthors

• George Barbastathis

  Massachusetts Institute of Technology

  67 shared

• Sunil Vyas

  National Taiwan University

  54 shared

• Din Ping Tsai

  34 shared

• Jennifer K. Barton

  31 shared

• Raymond K. Kostuk

  29 shared

• Cheng Hung Chu

  National Taiwan University

  23 shared

• J. Andrew Yeh

  Institute of Biological Chemistry, Academia Sinica

  21 shared

• Paul J. Gelsinger

  University of Arizona

  17 shared

Education

• Ph.D., College of Optical Sciences

  University of Arizona College of Optical Sciences

  2008

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