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jzhzhang · Jun 6, 2024 · 56f1dc8 · 56f1dc8
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               <p style="text-align:center">
                 <name>Jiazhao Zhang | 张嘉曌</name>
               </p>
-              <p>I am a Ph.D. student at the <a href="https://cfcs.pku.edu.cn/english/"> Center on Frontiers of Computing Studies </a>, Peking University, advised by Prof. <a href="https://hughw19.github.io/">He Wang</a> since 2022. Before this, I obtained my M.S. degree and B.Eng. degree from NUDT and Shandong University, respectively. During my master's studies, I was fortunate to be supervised by Prof. <a href="http://kevinkaixu.net/index.html">Kai Xu</a> and had the opportunity to work closely with Prof. <a href="http://www.zhuchenyang.net/">Chenyang Zhu</a>.
+              <p>I am a Ph.D. student at the <a  href="https://cfcs.pku.edu.cn/english/"> Center on Frontiers of Computing Studies </a> at Peking University, where I have been advised by Prof. <a href="https://hughw19.github.io/">He Wang</a> since 2022. Prior to this, I earned my M.S. degree from the National University of Defense Technology (NUDT), under the supervision of Prof. <a href="http://kevinkaixu.net/index.html">Kai Xu</a>. I received my B.Eng. degree from Shandong University.
               </p>
 
               <p>
-                I'm interested in indoor scene reconstruction, understanding and interaction. More specifically, I work on building robust and practical systems for home robots. 
+                My research goal is to develop intelligent and practical robots to enhance people's daily lives. My current research focuses on building intelligent navigation robots based on vision-language models. I am also interested in scene reconstruction and understanding, including techniques such as SLAM and segmentation.
               </p>
 
 
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               <br>
               <a href="https://arxiv.org/abs/2402.15852.pdf">Paper</a>
               /
-              Code
-              /
               <!-- <a href="https://github.com/yjtang249/MIPSFusion">Code</a> -->
               <a href="https://pku-epic.github.io/NaVid/">Webpage</a>
               <p></p>
               <p>
-                NaVid makes the first endeavour to showcase the capability of VLMs to achieve state-of-the-art level navigation performance without any maps, odometer and depth inputs. Following human instruction, NaVid only requires an on-the-fly video stream from a monocular RGB camera equipped on the robot to output the next-step action.
+                NaVid makes the first endeavour to showcase the capability of VLMs to achieve state-of-the-art level navigation performance without any maps, odometer and depth inputs. Following human instruction, NaVid only requires an on-the-fly video stream from a monocular RGB camera equipped on the robot to output the next-step action. 
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               <!-- / -->
               <a href="https://ieeexplore.ieee.org/document/9915552">Paper</a>
               /
-              Code & Data
+              <a href="[email protected]">Contact me for code permission</a>  
               <p></p>
               <p>
                 To realize efficient random optimization in the 18D state space of IMU tracking, we propose to identify and sample particles from active subspace.