diff --git a/index.html b/index.html
index 66616bc..358b6d7 100644
--- a/index.html
+++ b/index.html
@@ -37,7 +37,7 @@ <h2 id="publications">Publications</h2>
 <p><strong>Olivia Weng</strong>, Andres Meza, Quinlan Bock, Benjamin Hawks, Javier Campos, Nhan Tran, Javier Duarte, Ryan Kastner. <a href="/">FKeras: A Sensitivity Analysis Tool for Edge Neural Networks</a>. In submission.</p>
 </li>
 <li>
-<p>Colin Drewes, <strong>Olivia Weng</strong>, Andres Meza, Alric Althoff, Bill Hunter, David Kohlbrenner, Ryan Kastner, Dustin Richmond. <a href="https://arxiv.org/abs/2303.17881">Pentimento: Data Residue in Cloud FPGAs</a>. In submission.</p>
+<p>Colin Drewes, <strong>Olivia Weng</strong>, Andres Meza, Alric Althoff, Bill Hunter, David Kohlbrenner, Ryan Kastner, Dustin Richmond. <a href="https://arxiv.org/abs/2303.17881">Pentimento: Data Residue in Cloud FPGAs</a>. In <em>International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)</em>. To appear.</p>
 </li>
 <li>
 <p><strong>Olivia Weng</strong>, Gabriel Marcano, Vladimir Loncar, Alireza Khodamoradi, Abarajithan G, Nojan Sheybani, Andres Meza, Farinaz Koushanfar, Kristof Denolf, Javier Mauricio Duarte, and Ryan Kastner.
diff --git a/index.xml b/index.xml
index 1ac3742..f3b47eb 100644
--- a/index.xml
+++ b/index.xml
@@ -32,7 +32,7 @@ Before coming to UCSD, I received my BS in Computer Science at the University of
       <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
       
       <guid>http://oliviaweng.github.io/projects/</guid>
-      <description>FKeras: A Fault Tolerant Library for Understanding NN Resilience Workshop
+      <description>FKeras: A Fault Tolerant Library for Understanding NN Resilience Workshop paper
 Many scientific applications require neural networks (NNs) to operate correctly in safety-critical or high radiation environments, including automated driving, space, and high energy physics. For example, physicists at the Large Hadron Collider want to deploy an autoencoder to filter their experimental data at a high data rate (~40TB/s) in a high radiation environment. Thus, the autoencoder hardware must be both efficient and robust.</description>
     </item>
     
diff --git a/projects/index.html b/projects/index.html
index 7c429ba..7348e1a 100644
--- a/projects/index.html
+++ b/projects/index.html
@@ -31,7 +31,7 @@ <h1>Projects</h1>
 
 
 <h2 id="fkeras-a-fault-tolerant-library-for-understanding-nn-resilience">FKeras: A Fault Tolerant Library for Understanding NN Resilience</h2>
-<p><a href="/papers/radit2023.pdf">Workshop</a></p>
+<p><a href="/papers/radit2023.pdf">Workshop paper</a></p>
 <p>Many scientific applications require neural networks (NNs) to operate correctly in safety-critical or high radiation environments, including automated driving, space, and high energy physics.
 For example, physicists at the Large Hadron Collider want to deploy an autoencoder to filter their experimental data at a high data rate (~40TB/s) in a high radiation environment.
 Thus, the autoencoder hardware must be both efficient and robust.</p>