update

Author: jmduarte

people.html

@@ -227,11 +227,11 @@ <h3> MIT </h3>
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 		<h3> UC San Diego </h3>
-		<a href="javascript:showhide('jduarte')"> Javier Duarte</a>: PhD, Physics, Caltech
+		<a href="javascript:showhide('jduarte')"> Javier Duarte</a>: (PhD, Physics)
     	<div id="jduarte" style="display:none;">
 			<ul>
 				<li> <b>Position</b>: Assistant Professor </li>
-				<li> <b>Interests</b>: Higgs, trigger, graph neural networks, selective persistence, anomaly detection </li>
+				<li> <b>Interests</b>: Higgs boson, trigger, graph neural networks, selective persistence, data scouting, anomaly detection </li>
 			</ul>
 			<a href="images/javier_duarte.jpg" class="image image-centered"><img src="images/javier_duarte.jpg" height="300" alt="" /></a>
     	</div>

projects.html

@@ -47,11 +47,12 @@ <h2>Projects</h2>
 		</div>
 		<h3> Papers </h3>
 		<p style="line-height:1.3">
-			<ul>
-				<li> Fast inference of Boosted Decision Trees in FPGAs for particle physics. <a href="https://arxiv.org/abs/2002.02534">arXiv:2002.02534 [physics.comp-ph]</a>, February 2020. </li>
+		  <ul>
+		                <li> Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml, <a href="https://arxiv.org/abs/2003.06308">arXiv:2003.06308 [cs.LG]</a>, March 2020. </li>
+				<li> Fast inference of Boosted Decision Trees in FPGAs for particle physics, <a href="https://arxiv.org/abs/2002.02534">arXiv:2002.02534 [physics.comp-ph]</a>, February 2020. </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>.
-				<li> Accelerated Machine Learning as a Service for Particle Physics Computing. <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_64.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
-				<li> Low-latency machine learning inference on FPGAs. <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_74.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
+				<li> Accelerated Machine Learning as a Service for Particle Physics Computing, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_64.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
+				<li> Low-latency machine learning inference on FPGAs, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_74.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
 				<li> Fast inference of deep neural networks in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/13/07/P07027">JINST 13, P07027 (2018)</a> </li>
 				<li> FPGA-accelerated machine learning inference as a service for particle physics computing, <a href="https://doi.org/10.1007/s41781-019-0027-2">Comput Softw Big Sci (2019) 3: 13</a> </li>
 			</ul>