Add December talk

Author: diehlpk

index.html

@@ -51,6 +51,11 @@ <h2>Overview</h2>
       <td><b>Title</b></td>
       <td><b>Date</b></td>
   </tr>
+  <tr>
+      <td>Alejandra Castillo</td>
+      <td><a href="#fy275">Randomized Methods for Corrupted Tensor Systems</a></td>
+      <td>12/03/2025</td>
+  </tr>
   <tr>
       <td>Chen Zhang</td>
       <td><a href="#fy273">From Anaconda to Pixi: Modernizing Python Package Management for Neutron Science at ORNL</a></td>
@@ -186,6 +191,21 @@ <h2>Overview</h2>
 
 <h2>Talks</h2>
 
+<div id="fy275"></div>
+<h3>Randomized Methods for Corrupted Tensor Systems</h3>
+Speaker: Alejandra Castillo<br>
+Pomona College<br><br>
+
+Abstract:
+<p>
+Recovering tensor-valued signals from corrupted measurements is a central problem in various applications such as hyperspectral image reconstruction and medical imaging. This talk considers tensor linear systems of the form AX = B, that contain observations potentially affected by sparse, large-magnitude corruptions.  A quantile-based randomized Kaczmarz algorithm, called quantile tensor randomized Kaczmarz (QTRK), is discussed to address this challenge. By integrating quantile statistics into the iterative update process, QTRK improves robustness against adversarial errors. A variant selectively omits unreliable measurements to enhance stability further.
+</p>
+
+Bio:
+<p>
+Alejandra Castillo teaches mathematics and statistics at Pomona College. As a statistician, she is interested in applying statistical methodology to questions in biology and public health. Through more recent work, she has collaborated with applied mathematicians to extend linear-algebraic techniques to develop machine learning tools that promote transparency and efficiency in large-scale problems. She introduces students to computing and data science, and also supports efforts to broaden participation in the mathematical sciences.
+</p>
+
 <div id="fy273"></div>
 <h3>From Anaconda to Pixi: Modernizing Python Package Management for Neutron Science at ORNL</h3>
 Speaker: Chen Zhang<br>