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‎404.html‎

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<li><a href="/teaching/2026-spring-psy221f/">PSY-221F: Computational Neuroscience</a></li>
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<li><a href="/publications/2026-01-network-adaptive-cloud-processing/">Network-adaptive cloud preprocessing for visual neuroprostheses</a></li>
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<li><a href="/publications/2026-01-symbol-sight/">SymbolSight: Minimizing inter-symbol interference for reading with prosthetic vision</a></li>
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<li><a href="/publications/2026-01-network-adaptive-cloud-processing/">Network-adaptive cloud preprocessing for visual neuroprostheses</a></li>
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<li><a href="/teaching/2026-winter-cs181/">CS/ECE-181: Introduction to Computer Vision</a></li>
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<li><a href="/teaching/2026-winter-cs281b/">CS/ECE-281B: Advanced Topics in Computer Vision</a></li>

‎index.json‎

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‎people/beyeler_michael/index.html‎

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<a href="/publications/2026-01-network-adaptive-cloud-processing/" itemprop="url">Network-adaptive cloud preprocessing for visual neuroprostheses</a>
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<a href="/publications/2026-01-symbol-sight/" itemprop="url">SymbolSight: Minimizing inter-symbol interference for reading with prosthetic vision</a>
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<p class="m-0">We present a network-adaptive pipeline for cloud-assisted visual preprocessing of artificial vision, where real-time round-trip-time (RTT) feedback is used to dynamically modulate image resolution, compression, and transmission rate, explicitly prioritizing temporal continuity under adverse network conditions.</p>
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<p class="m-0">We present SymbolSight, a computational framework that selects symbol-to-letter mappings to minimize confusion among frequently adjacent letters. Using simulated prosthetic vision (SPV) and a neural proxy observer, we estimate pairwise symbol confusability and optimize assignments using language-specific bigram statistics.</p>
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<span itemprop="author name" itemtype="http://schema.org/Person">Jiayi Liu</span>, <span itemprop="author name" itemtype="http://schema.org/Person">Yilin Wang</span>, <span itemprop="author name" itemtype="http://schema.org/Person">
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<span itemprop="author name" itemtype="http://schema.org/Person">Jasmine Lesner</span>, <span itemprop="author name" itemtype="http://schema.org/Person">
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<a href="/people/beyeler_michael/">Michael Beyeler</a></span>
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<a href="/publications/2026-01-network-adaptive-cloud-processing/" >
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<a href="/publications/2026-01-symbol-sight/" >
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<a href="/publications/2026-01-symbol-sight/" itemprop="url">SymbolSight: Minimizing inter-symbol interference for reading with prosthetic vision</a>
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<a href="/publications/2026-01-network-adaptive-cloud-processing/" itemprop="url">Network-adaptive cloud preprocessing for visual neuroprostheses</a>
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<p class="m-0">We present SymbolSight, a computational framework that selects symbol-to-letter mappings to minimize confusion among frequently adjacent letters. Using simulated prosthetic vision (SPV) and a neural proxy observer, we estimate pairwise symbol confusability and optimize assignments using language-specific bigram statistics.</p>
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<p class="m-0">We present a network-adaptive pipeline for cloud-assisted visual preprocessing of artificial vision, where real-time round-trip-time (RTT) feedback is used to dynamically modulate image resolution, compression, and transmission rate, explicitly prioritizing temporal continuity under adverse network conditions.</p>
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<span itemprop="author name" itemtype="http://schema.org/Person">Jasmine Lesner</span>, <span itemprop="author name" itemtype="http://schema.org/Person">
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<span itemprop="author name" itemtype="http://schema.org/Person">Jiayi Liu</span>, <span itemprop="author name" itemtype="http://schema.org/Person">Yilin Wang</span>, <span itemprop="author name" itemtype="http://schema.org/Person">
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‎people/pogoncheff_galen/index.html‎

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<h3 class="fill-width">Honors &amp; Awards</h3>
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<li>CCS Community Fellowship (2026)</li>
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<li>Meta Summer Internship (2024, 2026)</li>
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<li>Outstanding TA Award, <em>CS, UCSB</em> (2024)</li>

‎people/varshney_apurv/index.html‎

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<p class="course">PhD in Computer Science, 2027 (expected)</p>
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<p class="course">PhD in Computer Science, 2028 (expected)</p>
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‎publication_types/6/index.html‎

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<h2><a href="/publications/2026-01-network-adaptive-cloud-processing/">Network-adaptive cloud preprocessing for visual neuroprostheses</a></h2>
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<h2><a href="/publications/2026-01-symbol-sight/">SymbolSight: Minimizing inter-symbol interference for reading with prosthetic vision</a></h2>
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We present a network-adaptive pipeline for cloud-assisted visual preprocessing of artificial vision, where real-time round-trip-time (RTT) feedback is used to dynamically modulate image resolution, compression, and transmission rate, explicitly prioritizing temporal continuity under adverse network conditions.
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We present SymbolSight, a computational framework that selects symbol-to-letter mappings to minimize confusion among frequently adjacent letters. Using simulated prosthetic vision (SPV) and a neural proxy observer, we estimate pairwise symbol confusability and optimize assignments using language-specific bigram statistics.
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<h2><a href="/publications/2026-01-network-adaptive-cloud-processing/">Network-adaptive cloud preprocessing for visual neuroprostheses</a></h2>
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We present SymbolSight, a computational framework that selects symbol-to-letter mappings to minimize confusion among frequently adjacent letters. Using simulated prosthetic vision (SPV) and a neural proxy observer, we estimate pairwise symbol confusability and optimize assignments using language-specific bigram statistics.
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We present a network-adaptive pipeline for cloud-assisted visual preprocessing of artificial vision, where real-time round-trip-time (RTT) feedback is used to dynamically modulate image resolution, compression, and transmission rate, explicitly prioritizing temporal continuity under adverse network conditions.
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