<title> Xinghui Zhong</title>

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<h1>Xinghui Zhong (仲杏慧）</h1>

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<h2>Contact info:</h2>

<font size="4"><a href="http://www.math.zju.edu.cn/">School
of Mathematical Sciences</a><br>
<br>
<a href="http://www.zju.edu.cn/">Zhejiang
University</a><br>
<br>
Hangzhou, China <br>
<br>
Office: Room 714, Haina Complex Building 2 <br>
<br>
Email: zhongxh
at zju.edu.cn <br>
<br>
Phone：0571-87952335(8204)<br>
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<br>
<H2>Academic Background</H2>
Xinghui Zhong received her B.S. degree in Mathematics from the <A
href="http://en.ustc.edu.cn/">University of Science and Technology of China</A> in 2007. In 2012 she received her Ph.D degree from the <a href="http://www.dam.brown.edu/">Division of Applied Mathematics</a> of </span></span><a
href="https://www.brown.edu/">Brown University</a> under the supervision of Professor <a href="https://www.dam.brown.edu/people/shu/">Chi-Wang Shu</a>. She then spent
three years at the <a href="https://math.msu.edu/">Department
of Mathematics</a>
in <a href="https://msu.edu/">Michigan State University</a> and one year at the <a
href="https://sci.utah.edu/">Scientific Computing and Imaging Institute </a> of <a
href="https://www.utah.edu/">The University of Utah</a> as a postdoctoral fellow. Since
2016, she has been with the <a href="http://www.math.zju.edu.cn/">School
of Mathematical Sciences</a>,
<a href="http://www.math.zju.edu.cn/">Zhejiang University</a> as a Special-Termed Professor (2016-2022), and Associate Professor (2023-). She works in the area of numerical analysis, scientific computing, uncertainty quantification and machine learning. Her research is focused on the design, analysis and applications of accurate and efficient numerical methods for partial differential equations in various applications.
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<UL>
<LI> <A HREF="pub.htm"> List of Publications </A>
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<UL>
<LI> <A HREF="member.htm"> Group Members </A>
</UL>
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<LI> <A HREF="teaching.htm"> Teaching </A>
</UL>
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<H2>Research Interests</H2>

<UL>
<LI>Numerical solutions of conservation laws and in general convection dominated problems using:
<P>
<UL>
<LI>  Discontinuous Galerkin methods
 <LI> Weighted essentially non-oscillatory (WENO) methods
<LI>Spectral methods
</UL>
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<LI> Numerical simulations of kinetic equations
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<LI> Uncertainty quantification and stochastic computations
    <p>
<LI> Machine learning

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