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<div class="u-image u-image-circle u-image-1" alt="" data-image-width="192" data-image-height="192"></div>
<h3 class="u-custom-font u-text u-text-1">Hongyi li (李泓仪)</h3>
<p class="u-align-left u-custom-font u-font-pt-sans u-text u-text-2"> M.Eng. Student (2019-current)<br>The State Key Laboratory of ISN<br>Xidian University<br>Xi’an, China<br>Email: [email protected]<br><a href="https://github.com/VistaLee/Hongyi_Li.github.io/blob/main/supplementary_material/Hongyi_CV.pdf" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-2">CV</a>
</p>
</div>
</div>
<div class="u-align-justify u-container-style u-layout-cell u-size-42 u-layout-cell-2">
<div class="u-container-layout u-container-layout-2">
<h4 class="u-custom-font u-font-montserrat u-text u-text-3"> Biography</h4>
<p class="u-text u-text-4"> Hongyi Li is a M.Eng. student at Xidian University, China, supervised by Professor <a href="https://web.xidian.edu.cn/ychwang/index.html" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-1">Yongchao Wang</a>. She is also a visiting student at Emory University since 2020, and her foreign advisor is Professor <a href="http://cs.emory.edu/~lzhao41/" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-2">Liang Zhao</a>. Before that, she received the B.Eng. degree (Hons.) in Telecommunications Engineering from Xidian University and graduated as the valedictorian of Xidian in 2019.
</p>
<h4 class="u-custom-font u-font-montserrat u-text u-text-5"> News</h4>
<ul class="u-text u-text-6">
<li>04/2023: I serve as the PC member of the OPT for Machine Learning NeurIPS Workshop 2023.
</li>
<li>11/2022: Our paper "Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework" is accepted by TNNLS.
</li>
<li>05/2022: I serve as the PC member of the OPT for Machine Learning NeurIPS Workshop 2022.
</li>
</ul>
<h4 class="u-custom-font u-font-montserrat u-text u-text-5"> Publications</h4>
<h5 class="u-custom-font u-font-montserrat u-text u-text-6">Journals</h5>
<ul class="u-text u-text-6">
<li> Junxiang Wang, <span style="font-weight: 700;">Hongyi Li (first-coauthor),</span> Zheng Chai, Yongchao Wang, Yue Cheng, and Liang Zhao. Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework, IEEE Transactions on Neural Networks and Learning Systems <span style="font-weight: 700;"><em>(TNNLS)</em></span>, (Impact Factor: 14.255), accepted. <a href="https://www.researchgate.net/publication/351744585_Towards_Quantized_Model_Parallelism_for_Graph-Augmented_MLPs_Based_on_Gradient-Free_ADMM_framework" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-3">[paper]</a>
</li>
<li> Junxiang Wang, <span style="font-weight: 700;">Hongyi Li</span>, and Liang Zhao.Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. <span style="font-weight: 700;"><em>Neurocomputing</em></span>, (Impact Factor: 5.719), accepted. <a href="https://www.researchgate.net/publication/358622894_Accelerated_Gradient-free_Neural_Network_Training_by_Multi-convex_Alternating_Optimization" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-3">[paper]</a>
</li>
<li> Junji Jiang, Chen Ling, <span style="font-weight: 700;">Hongyi Li</span>, Guangji Bai, Xujiang Zhao, and Liang Zhao. Quantifying Uncertainty in Graph Neural Network Explanations. <span style="font-weight: 700;"><em>Frontiers in Big Data</em></span>, (Impact Factor: 3.1), accepted.<a href="https://www.frontiersin.org/articles/10.3389/fdata.2024.1392662/pdf"class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-3">[paper]</a>
</li>
</ul>
<h5 class="u-custom-font u-font-montserrat u-text u-text-6">Workshops</h5>
<ul class="u-text u-text-6">
<li> <span style="font-weight: 700;">Hongyi Li</span>, Junxiang Wang, Yongchao Wang, Yue Cheng, and Liang Zhao. Community-based Layerwise Distributed Training of Graph Convolutional Networks. NeurIPS 2021 Workshop on Optimization for Machine Learning <span style="font-weight: 700;"><em>(OPT 2021)</em></span>. <a href="https://github.com/VistaLee/Hongyi_Li.github.io/blob/main/supplementary_material/NeurIPS%20OPT2021/ADMM_GNN_training.pdf" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-3">[paper]</a><a href="https://github.com/VistaLee/Hongyi_Li.github.io/blob/main/supplementary_material/NeurIPS%20OPT2021/poster_opt21.pdf" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-3">[poster]</a>
</li>
<li>
Junxiang Wang, </span>
<span style="font-weight: 700;">Hongyi Li,</span> Yongchao Wang, and Liang Zhao. Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. Accepted by Workshop on ”Beyond first-order methods in ML systems” of the 38th International Conference on Machine Learning. <a href="https://arxiv.org/abs/1811.04187" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-4">[paper]</a>
<a href="https://github.com/VistaLee/Hongyi_Li.github.io/blob/main/supplementary_material/ICMLOPT2021/DLAM-slides.pptx" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-5">[slides]</a>
<a href="https://github.com/VistaLee/Hongyi_Li.github.io/blob/main/supplementary_material/ICMLOPT2021/DLAM-Workshop.mp4" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-6">[video]</a>
</li>
</ul>
<h5 class="u-custom-font u-font-montserrat u-text u-text-6">Preprints</h5>
<ul class="u-text u-text-6">
<li> <span style="font-weight: 700;">Hongyi Li</span>, Junxiang Wang, and Yongchao Wang. Edge Graph Neural Networks for Massive MIMO Detection. Preprint. <a href="https://www.researchgate.net/publication/361300344_Edge_Graph_Neural_Networks_for_Massive_MIMO_Detection" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-3">[paper]</a>
</li>
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