<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Seminar | MOL</title><link>https://matrixopticslab.github.io/tag/seminar/</link><atom:link href="https://matrixopticslab.github.io/tag/seminar/index.xml" rel="self" type="application/rss+xml"/><description>Seminar</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 28 Dec 2025 00:00:00 +0000</lastBuildDate><image><url>https://matrixopticslab.github.io/media/icon_hu17334560117968675030.png</url><title>Seminar</title><link>https://matrixopticslab.github.io/tag/seminar/</link></image><item><title>Computational Optics Wavefront Sensing and Phase Imaging Technology</title><link>https://matrixopticslab.github.io/post/liangcaicao/</link><pubDate>Sun, 28 Dec 2025 00:00:00 +0000</pubDate><guid>https://matrixopticslab.github.io/post/liangcaicao/</guid><description>&lt;p>On December 28, 2025, we were honored to host &lt;strong>Prof. Liangcai Cao&lt;/strong> from the Department of Precision Instruments at &lt;strong>Tsinghua University&lt;/strong> for an academic seminar titled &lt;strong>&amp;ldquo;Computational Optics Wavefront Sensing and Phase Imaging Technology.&amp;rdquo;&lt;/strong>&lt;/p>
&lt;p>Prof. Cao is a renowned expert in holographic display and computational imaging. During his presentation, he shared his group&amp;rsquo;s latest breakthroughs in integrating physical models with neural networks, specifically highlighting:&lt;/p>
&lt;ul>
&lt;li>&lt;strong>Propagation-Adaptive Holography&lt;/strong>: Utilizing physics-constrained spatial and Fourier neural operators to achieve high-fidelity 4K computer-generated holography.&lt;/li>
&lt;li>&lt;strong>Computational Wavefront Sensing&lt;/strong>: Implementing high-resolution wavefront characterization for complex wavefields in biological imaging.&lt;/li>
&lt;li>&lt;strong>Physics-Informed Deep Learning&lt;/strong>: Applications in adaptive aberration-corrected quantitative phase microscopy to overcome traditional imaging limits.&lt;/li>
&lt;/ul>
&lt;p>The seminar sparked extensive discussions among our group members regarding the intersection of &lt;strong>random matrix theory&lt;/strong> and &lt;strong>holographic reconstruction&lt;/strong>. We sincerely thank Prof. Cao for his profound insights and the valuable academic exchange.&lt;/p></description></item><item><title>Optical Coherence Tomography: Technology and Applications</title><link>https://matrixopticslab.github.io/post/zhihuading/</link><pubDate>Thu, 04 Dec 2025 00:00:00 +0000</pubDate><guid>https://matrixopticslab.github.io/post/zhihuading/</guid><description>&lt;p>On December 4, 2025, our group was honored to host &lt;strong>Prof. [cite_start]Zhihua Ding&lt;/strong> from the College of Optical Science and Engineering at &lt;strong>Zhejiang University&lt;/strong> for an academic seminar titled &lt;strong>&amp;ldquo;Optical Coherence Tomography: Technology and Applications&amp;rdquo;&lt;/strong>.&lt;/p>
&lt;p>[cite_start]Prof. Ding is a distinguished expert in biomedical photonics, with extensive research experience at institutions such as Shizuoka University and the Beckman Laser Institute at UC Irvine. [cite_start]During the seminar, he discussed the transformative potential of &lt;strong>Optical Coherence Tomography (OCT)&lt;/strong> as a label-free, high-resolution, and high-speed 3D imaging modality for both biomedical and industrial sectors. Key highlights of his presentation included:&lt;/p>
&lt;ul>
&lt;li>&lt;strong>Ultra-deep 3D OCT Imaging&lt;/strong>: Latest research achievements in achieving superior imaging depth within highly turbid media.&lt;/li>
&lt;li>[cite_start]&lt;strong>Clinical Diagnostics&lt;/strong>: Progress in utilizing fiber-probe-based quantitative OCT for the detection of cervical cancer.&lt;/li>
&lt;li>[cite_start]&lt;strong>System Innovation&lt;/strong>: Development of advanced multimodal systems, such as intravascular ultrasound-OCT endoscopes and multi-scale brain imaging devices.&lt;/li>
&lt;/ul>
&lt;p>[cite_start]The session concluded with an engaging discussion on breaking the current limits of optical imaging depth to open new frontiers in scientific research and medical applications. [cite_start]We thank Prof. Ding for his inspiring presentation and his contribution to the advancement of OCT technologies.&lt;/p></description></item><item><title>Deep Holography, Quantum Imaging and Lensless 3D Endomicroscopy towards Advances in Biomedicine</title><link>https://matrixopticslab.github.io/post/juergenczarske/</link><pubDate>Thu, 09 Oct 2025 00:00:00 +0000</pubDate><guid>https://matrixopticslab.github.io/post/juergenczarske/</guid><description>&lt;p>On October 9, 2025, we were honored to host &lt;strong>Prof. [cite_start]Juergen Czarske&lt;/strong> from the &lt;strong>Dresden University of Technology (TUD)&lt;/strong>, Germany, for an inspiring academic seminar titled &lt;strong>&amp;ldquo;Deep Holography, Quantum Imaging and Lensless 3D Endomicroscopy towards Advances in Biomedicine&amp;rdquo;&lt;/strong>[cite: 1, 3].&lt;/p>
&lt;p>[cite_start]Prof. Czarske is a Fellow of OPTICA, SPIE, and several other prestigious societies, and the recipient of the 2024 SPIE Dennis Gabor Award in Diffractive Optics[cite: 13, 15]. [cite_start]During the seminar, he presented a paradigm shift in biomedical imaging, moving from traditional bulky lens-based systems to &lt;strong>lensless fiber endoscopes&lt;/strong> enabled by &lt;strong>computational imaging&lt;/strong>[cite: 7, 8]. Key highlights included:&lt;/p>
&lt;ul>
&lt;li>&lt;strong>Physics-Informed Deep Learning&lt;/strong>: Exploiting speckle patterns to achieve high-fidelity 3D imaging and &lt;strong>virtual staining&lt;/strong> of cancer cells[cite: 8, 9, 10].&lt;/li>
&lt;li>[cite_start]&lt;strong>Quantum Imaging Perspectives&lt;/strong>: Discussing the potential of using entangled photons and teleportation for advanced biomedical sensing[cite: 11].&lt;/li>
&lt;li>[cite_start]&lt;strong>Clinical Potential&lt;/strong>: How these advancements can detect and prevent disease development through early and gentle diagnostics[cite: 6].&lt;/li>
&lt;/ul>
&lt;p>[cite_start]The presentation sparked deep discussions on the intersection of &lt;strong>deep holography&lt;/strong> and our group&amp;rsquo;s work in random matrix theory[cite: 12]. [cite_start]We thank Prof. Czarske for his insightful talk and for fostering international academic exchange between TUD, SIOM, and our laboratory[cite: 3, 5].&lt;/p></description></item><item><title>Snapshot Compressive Imaging: From Theory to Applications</title><link>https://matrixopticslab.github.io/post/25-03-21-yuanxin/</link><pubDate>Fri, 21 Mar 2025 00:00:00 +0000</pubDate><guid>https://matrixopticslab.github.io/post/25-03-21-yuanxin/</guid><description>&lt;p>On March 21, 2025, our group was honored to host &lt;strong>Prof. Xin Yuan&lt;/strong> from the School of Engineering at &lt;strong>Westlake University&lt;/strong> for an engaging academic seminar titled &lt;strong>&amp;ldquo;Snapshot Compressive Imaging: From Theory to Applications.&amp;rdquo;&lt;/strong>&lt;/p>
&lt;p>Prof. Yuan is a leading expert in the field of computational imaging and compressive sensing. During his presentation, he provided a comprehensive overview of his team&amp;rsquo;s significant contributions to &lt;strong>Snapshot Compressive Imaging (SCI)&lt;/strong>, focusing on:&lt;/p>
&lt;ul>
&lt;li>&lt;strong>Hardware-Algorithm Co-design&lt;/strong>: Innovative architectures for capturing high-dimensional data (video, hyperspectral) using 2D detectors through coded apertures.&lt;/li>
&lt;li>&lt;strong>Deep Learning for SCI&lt;/strong>: The transition from traditional optimization-based reconstruction to state-of-the-art deep learning models (such as PnP-ADMM and Transformers) for real-time, high-quality recovery.&lt;/li>
&lt;li>&lt;strong>Clinical and Industrial Applications&lt;/strong>: How SCI technology is revolutionizing ultra-high-speed photography and biomedical imaging, providing new tools for observing fast-evolving biological processes.&lt;/li>
&lt;/ul>
&lt;p>The seminar concluded with an interactive session where members of the &lt;strong>Random Matrix Optics Research Group (RMORG)&lt;/strong> discussed the potential of combining SCI with &lt;strong>statistical optics&lt;/strong> to tackle deep-tissue imaging challenges. We extend our gratitude to Prof. Yuan for sharing his visionary work.&lt;/p></description></item></channel></rss>