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@@ -48,9 +48,10 @@ <h2>Projects</h2>
 		<h3> Papers </h3>
 		<p style="line-height:1.3">
 		  <ul>
-		    <li> Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics, <a href="https://arxiv.org/abs/2008.03601">arXiv:2008.03601 [hep-ex]</a>. </li>
-		    <li> GPU coprocessors as a service for deep learning inference in high energy physics, <a href="https://arxiv.org/abs/2007.10359">arXiv:2007.10539 [physics.comp-ph]</a>. </li>
-		    <li> Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml, <a href="https://arxiv.org/abs/2006.10159">arXiv:2006.10159 [physics.ins-det]</a>. </li>
+		    <li> GPU-accelerated machine learning inference as a service for computing in neutrino experiments, <a href="https://arxiv.org/abs/2009.04509">arXiv:2009.04509</a> [physics.comp-ph]. </li>
+		    <li> Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics, <a href="https://arxiv.org/abs/2008.03601">arXiv:2008.03601</a> [physics.comp-ph]. </li>
+		    <li> GPU coprocessors as a service for deep learning inference in high energy physics, <a href="https://arxiv.org/abs/2007.10359">arXiv:2007.10539</a> [physics.comp-ph]. </li>
+		    <li> Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml, <a href="https://arxiv.org/abs/2006.10159">arXiv:2006.10159</a> [physics.ins-det]. </li>
 		    <li> Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml, <a href="https://doi.org/10.1088/2632-2153/aba042">MLST (2020)</a>. </li>
 		    <li> Fast inference of Boosted Decision Trees in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/15/05/p05026">JINST 15, P05026 (2020)</a>. </li>
 		    <li> ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning, <a href="https://sld.cs.columbia.edu/pubs/giri_date20.pdf"> DATE Conference 2020 </a>.