Update index.html

index.html

@@ -86,17 +86,19 @@ <h3><b>RUIQI</b></h3>
       <img style="width: 80%;max-width: 320px" alt="profile photo" src="images/RuiqiZhang.jpg">
       <h1>Ruiqi Zhang</h1>
         <p class="w3-justify" style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;max-width:600px">
-          I am a second-year Ph.D. in the Department of Statistics at University of California, Berkeley, advised by Prof. <a href="https://www.stat.berkeley.edu/~bartlett/">Peter L. Bartlett</a></a>.
-          Previously, I got my bachelor's degree in the School of Mathematical Science(SMS) at Peking University(PKU), majoring Mathematics and Statistics.
+          I am a second-year Ph.D. in the Department of Statistics at University of California, Berkeley, advised by Prof. <a href="https://www.stat.berkeley.edu/~bartlett/">Peter L. Bartlett</a></a>
+          and Prof. <a href="https://www.stat.berkeley.edu/~songmei/">Song Mei</a></a>
+          Previously, I got my bachelor's degree in the School of Mathematical Science(SMS) at Peking University(PKU), majoring in Mathematics and Statistics.
           <br>
           <br>
-          My research mainly focuses on the theory and application of modern Machine Learning(ML), Deep Learning(DL), and Large Language Models(LLMs). More specifically, currently, I focus on
+          My research mainly focuses on the theory and application of modern Machine Learning(ML), Deep Learning(DL), and Large Language Models(LLMs). 
+          More specifically, currently, I focus on 
           <br>
         </p>
 
       <!-- Bullet points start here -->
       <ul style="width:100%;max-width:600px;margin-right:auto;margin-left:auto;">
-        <li><strong>The Theory for Transformers and In-Context Learning.</strong></li>
+        <li><strong>The Theory for Transformers, In-Context Learning, and Scaling Laws.</strong></li>
         <li><strong>Scalable and Efficient Alignment for Large Language Models.</strong></li>
         <li><strong>Topics in Deep Learning Theory, such as Implicit Bias and Edge of Stability.</strong></li>
         <li><strong>Sequential Decision Making in Bandits and Reinforcement Learning.</strong></li>
@@ -105,9 +107,9 @@ <h1>Ruiqi Zhang</h1>
       <!-- Bullet points end here -->
 
         <p class="w3-center">
-          <a href="mailto:[email protected]">Email</a> &nbsp/&nbsp Language: Chinese, English, French &nbsp/&nbsp Coding: Python, R, Matlab, LaTeX, Prompting GPT. 
+          <a href="mailto:[email protected]">Email</a> &nbsp/&nbsp Language: Chinese, English. &nbsp/&nbsp Coding: Python, R, Matlab, LaTeX, Prompting GPT. 
           <br>
-          Reviewer experience: ICML 2022,2024, NIPS 2023, NIPS 2023 R0-FoMo Workshop, NIPS 2023 Math+A Workshop, ICLR 2024, AISTATS 2024, TMLR.
+          Reviewer experience: ICML 2022,2024, NIPS 2023,2024, NIPS 2023 R0-FoMo Workshop, NIPS 2023 Math+A Workshop, ICLR 2024, AISTATS 2024, TMLR, DMLR.
         </p>
         </tbody></table>
   </div>
@@ -123,7 +125,7 @@ <h2>Publication</h2>
       <br>
       <strong>Ruiqi Zhang*</strong>, Licong Lin*, Yu Bai, Song Mei <em> (* for co-first authors).</em>
       <br>
-      <em>Submitted, 2024</em> | <a style="color: #447ec9" href="https://arxiv.org/abs/2404.05868">Paper</a>
+      <em>COLM, 2024</em> | <a style="color: #447ec9" href="https://arxiv.org/abs/2404.05868">Paper</a>
       </p>    
 
     <p>