update group

group.html

@@ -70,18 +70,18 @@ <h2><hr>PhD (visiting/external co-supervisor/close collaboration)</h2>
 
          <h2><hr>Master</h2>
         <ul>
-            <li> Zhengyang Ren (Co-supervision <a href="https://fengliu90.github.io/">Feng Liu</a>) </li>
-            <li> Zhenchen Wan (Co-supervision <a href="https://fengliu90.github.io/">Feng Liu</a>) </li>
-            <li> Haoyuan Li (Co-supervision <a href="https://fengliu90.github.io/">Feng Liu</a> and <a href="https://www.wehi.edu.au/people/andrew-webb">Andrew I. Webb</a>) </li>
-            <li> Feng Hu (Co-supervision <a href="https://fengliu90.github.io/">Feng Liu</a>) </li>
-            <li>Lily Li (Co-supervision <a href="https://people.eng.unimelb.edu.au/baileyj/">James Bailey</a> and <a href="https://www.doherty.edu.au/people/professor-lachlan-coin">Lachlan Coin</a>) </li>
+            <li> Zhengyang Ren (Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a>) </li>
+            <li> Zhenchen Wan (Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a>) </li>
+            <li> Haoyuan Li (Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a> and <a href="https://www.wehi.edu.au/people/andrew-webb">Andrew I. Webb</a>) </li>
+            <li> Feng Hu (Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a>) </li>
+            <li>Lily Li (Co-supervision with <a href="https://people.eng.unimelb.edu.au/baileyj/">James Bailey</a> and <a href="https://www.doherty.edu.au/people/professor-lachlan-coin">Lachlan Coin</a>) </li>
             <li>Steven Luo (Co-supervision with <a href="https://sites.google.com/view/liuhua-peng/home">Liuhua Peng</a>) </li>
             <li>Ruslan Mikhailov (2024)</li>
             <li>Jiaqi Wu (2024; Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a>)
             <li>Xinyu Li (2024; Co-supervision with <a href="https://sites.google.com/view/liuhua-peng/home">Liuhua Peng</a> and <a href="https://fengliu90.github.io/">Feng Liu</a>)
             <li>Ron Chen (2024; Co-supervision with <a href="https://sites.google.com/view/liuhua-peng/home">Liuhua Peng</a>; -> Susquehanna International Group)</li>
             <li>Lingxiao Li (2024; Co-supervision with <a href="https://people.eng.unimelb.edu.au/baileyj/">James Bailey</a>)</li>
-            <li>Ziyang Huang (2023; Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a>)</li>
+            <li>Ziyang Huang (2023; Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a> -> AIBUILD)</li>
             <li>Zexu Huang (2023; Co-supervision with <a href="https://people.eng.unimelb.edu.au/smonazam/">Sarah Erfani</a>; -> PhD at University of Technology Sydney)</li>
             <li>Tianyu Huang (2023; Co-supervision with <a href="https://fengliu90.github.io/">Feng Liu</a>; -> PhD at University of Sydney)</li>
             <li>Kuoyuan Li (2023; Co-supervision with <a href="http://www.kehinger.com/">Krista A. Ehinger</a>; -> Seminal)</li>

index.html

@@ -82,7 +82,7 @@ <h1 style="margin-top: 0em">Home</h1><br>
     <h2><hr><a name="research"></a>Research Interests</h2>
      <p>
 
-I am dedicated to providing theoretical foundations and computational innovations in understanding the generative process of real-world complex data. My research focuses on two levels of generative models. The first level involves learning from observational data distributions to sample new data. The second level involves generative models that depict the underlying causal structure of the data distribution. Causal generative models go beyond mere data generation; they enable intervention in the system by manipulating variables and observing the consequences. I aim to explore the causal generative process of data to learn causal representations that are transferable, robust, and interpretable. Additionally, I am interested in designing learning models tailored for various types of real data, including but not limited to image and video data, 3D data, time series data, and biological data.
+I am dedicated to providing theoretical foundations and computational innovations in understanding the generative process of real-world complex data. My research focuses on two levels of generative models. The first level involves learning from observational data distributions to sample new data. The second level involves generative models that depict the underlying causal structure of the data distribution. Causal generative models go beyond mere data generation; they enable intervention in the system by manipulating variables and observing the consequences. I aim to explore the causal generative process of data to learn causal representations that are transferable, robust, and interpretable. Additionally, I am interested in designing learning models tailored for various types of real data, including but not limited to image and video data, 3D data, time series data, biological data, and multi-modal data.
     </p>
     <ul>
     <li><p>Causal Learning: Causal discovery & inference, Causal generative models, Causal fairness, Causal representation learning</p></li>