个人简介
Welcome to my homepage.
Starting from 2025, there could be 12 open PhD positions in my group. The topics are applied and computational mathematics, with particular focus on numerical methods for partial differential equations and/or for inverse problems, optimal control and optimization, which can be quite flexible. If you are interested in the exploration of such topics, please email me with your CV including a brief research statement to guozhi.dong@csu.edu.cn
I am currently a tenure track associate professor at the School of Mathematics and Statistics, Central South University (CSU), Changsha.
Before joining CSU, I was a research scientist at Humboldt University of Berlin from 20172022, and also affiliated with Weierstrass Institute in Berlin, working with Professor Michael Hintermüller. I was also a member of Berlin Mathematics Research Center, which is one of the Excellence Cluster funded by Germany Science Fundation (DFG).
Between 20122017, I was a research asistant at the Computational Science Center, University of Vienna, where I earned my PhD degree (with distinction) under the supervision of Professor Otmar Scherzer. Earlier than that, I had been working as an assistant (secretary) on scientific affairs in the Faculty of Mathematics and Computer Sciences from 20072012 at Hunan Normal University, Changsha, China, where I obtained both my Bachelor degree (2007) and Master degree (2012). During 20102011, I had a chance to visit the University of Eastern Finland, Kuopio campus, where I was exposed to the topic of Inverse Problems for the first time.
Research experience
I have working experience in a few tightly connected areas in applied and computational mathematics: Inverse and imaging problems, their variational regularization methods, with connections to some dynamical geometric partial differential equations (PDEs); Optimal control of PDEs and optimization with PDE constraints; Numerical solutions of PDEs, particularly for secondorder dissipative hyperbolic PDEs, and those solutions and data on manifolds; Mathematics of deep learning and their applications in scientific computing and imaging. My research results are published in international journals or conferences on computational and applied mathematics of highest quality.
Hobby
I always feel a lot of fun from sports, including many kinds of ball games, range from tiny, e.g., table tennis, to large, e.g., basketball, but not limitted to these. For instance, swiming, playing chess/cards, jogging, skating, music are also my hobbies. I like literature and poem. Sometimes, I write poems, but the frequency becomes less and less. I could have become a writer or a poet if I did not choose to be a mathematician. When I have time, I enjoy cooking for my family and friends as well.
教育经历
[1] 2017.92019.9
柏林洪堡大学 博士后
[2] 2012.102017.3
维也纳大学  数学  博士学位  博士研究生毕业
[3] 2009.42012.6
湖南师范大学  理学  硕士学位  硕士研究生毕业
[4] 2010.92011.3
东芬兰大学.  理学 硕士研究生结业
[5] 2003.92007.6
湖南师范大学  理学  学士学位  本科(学士)
工作经历
[1] 2022.4至今
中南大学

数学与统计学院

在职
[2] 2019.102021.12
魏尔斯特拉斯研究所

第八组

助理研究员
[3] 2017.92022.3
柏林洪堡大学

数学学院

助理研究员
[4] 2012.102017.8
维也纳大学

数学学院

研究助理
[5] 2007.72012.9
湖南师范大学

科研与研究生办

（科研秘书）实习研究员
社会兼职

[1] 2024.6至今
中国仿真学会不确定性系统分析与仿真专委会委员 
[2] 2023.3至今
CSIAM 反问题与成像专委会委员 
[3] 2019.4至今
Reviewer for AMS Mathematical Review
研究方向
其他联系方式
[6] 邮箱：
团队成员
团队名称：Computational models based on secondorder hyperbolic PDEs and their numerical algorithms
团队介绍：This is a new research direction which we have put efforts on. Thanks to my excellent collaborators and students, in particular Dr. Wei Liu and Mr. Haifan Chen, Mr. Zikang Gong, we find a lot of exciting problems to work on.
团队名称：Machine learning methods in inverse problems, optimal control of partial differential equations
团队介绍：We combine the power of machine learning techeniques from modelling to computational aspects with classical methods in inverse problems and mathematical imaging, as well as optimal control of partial differential equations.
团队名称：Numerical methods for direct and inverse problems of PDEs on manifolds
团队介绍：We develop numerical methods for partial differential equations defined on surfaces or general manifolds. Based on that, our experiences will be extended to variational problems, and inverse problems involving PDEs on manifolds.