董国志科研成果

个人简介 Welcome to my homepage.  I am currently a tenure track associated 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 2017--2022, 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). I was a research asistant at the Computational Science Center, University of Vienna from 2012--2017, 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 2007--2012 at Hunan Normal University, Changsha, China, where I obtained both my Bachelor degree (2007) and Master degree (2012). During 2010-2011, 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; Optimal control of partial differential equations and optimization with PDE constraints; Numerical solutions of (partial) differential equations, particularly for 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 on computational and applied mathematics of highest quality, for instance: ESAIM: COCV,  Found. Comput. Math., Inver. Probl. Imag., J. Comput. Phys., SIAM J. Imag. Science, SIAM J. Sci. Comput., SIAM J. Opt. among others. 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, 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]   2017.9-2019.9 柏林洪堡大学 博士后 [2]   2012.10-2017.3 维也纳大学  |  数学  |  博士学位  |  博士研究生毕业 [3]   2009.4-2012.6 湖南师范大学  |  理学  |  硕士学位  |  硕士研究生毕业 [4]   2010.9-2011.3 东芬兰大学.  |  理学 硕士研究生结业 [5]   2003.9-2007.6 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]   2019.10-2021.12 魏尔斯特拉斯研究所  |  第八组  |  助理研究员 [3]   2017.9-2022.3 柏林洪堡大学  |  数学学院  |  助理研究员 [4]   2012.10-2017.8 维也纳大学  |  数学学院  |  研究助理 [5]   2007.7-2012.9 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]   2023.3-至今     CSIAM 反问题与成像专委会委员 [2]   2019.4-至今     Reviewer for AMS Mathematical Review 研究方向 [1]  Inverse Problems and mathematical imaging [2]  Optimal control and PDE constrained optimization [3]  Numerical methods for partial differential equations [4]  Mathematics of deep learning and their applications 其他联系方式 [6]  邮箱： guozhi.dong@csu.edu.cn 团队成员 团队名称：Computational models based on second-order 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.