Biography submitted by Dr. Hong:
Dr. Qingguo Hong, assistant professor of mathematics and statistics, hails from Hunan, China. He began his academic journey at Xiangtan University, an institution renowned for producing exceptional mathematicians, including luminaries like Yaxiang Yuan (President of the International Council for Industrial and Applied Mathematics, Member of the Chinese Academy of Sciences, SIAM Fellow, AMS Fellow) and Jinchao Xu (Academia Europaea Member, SIAM Fellow, AMS Fellow, AAAS Fellow). It was their encouragement that led Qingguo to delve into the realms of information and computational science. Subsequently, he pursued his M.S. in Computational Mathematics at Xiangtan University, where, during his master’s program, he devised a numerical method for approximating solutions to the fourth-order curl problem, with applications in magnetohydrodynamics. Upon completing his studies at Xiangtan University, Qingguo embarked on his doctoral journey in Computational Mathematics at Peking University, under the guidance of Professors Jun Hu and Jinchao Xu. At Peking University, he focused on the development of stable numerical methods for nonlinear incompressible elasticity and viscoelasticity flow models. He successfully earned his Ph.D. from Peking University in 2012. Following this, he assumed the role of a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics at the Austrian Academy of Sciences. In 2016, he transitioned to the Faculty of Mathematics at The University of Duisburg-Essen, working as a Postdoctoral Scholar. Prior to joining Missouri S&T, Dr. Hong held positions as a Postdoctoral Scholar and an Assistant Research Professor at The Pennsylvania State University.
Dr. Hong’s research encompasses both theoretical and practical aspects, focusing on the numerical analysis and mathematical foundations of machine learning, and its diverse applications in fields such as numerical partial differential equations, data fitting, and image classification. His work is underpinned by the establishment of connections between machine learning and numerical theories, including finite element analysis, numerical integration, iterative methods, and matrix analysis. These connections serve as the driving force behind leveraging the potent approximation capabilities of neural networks to solve high-order partial differential equations, design novel training algorithms for optimizing machine learning problems, and develop more efficient neural network architectures. Recent publications by Dr. Hong include research on the stability analysis of perturbed saddle-point problems with applications in Mathematics of Computation, as well as research on utilizing machine learning to solve PDEs in the Journal of Computational Physics.
In addition to his ongoing scholarly pursuits at Missouri S&T, Dr. Hong is actively involved in teaching within the Department of Mathematics and Statistics. Presently, he instructs two sections’ course on Elementary Differential Equations. He deeply appreciates the warm reception he has received from his colleagues, the department, the college, and the university. Qingguo is also greatly impressed by the diligence and achievements of his students and is enthusiastic about creating a classroom environment that nurtures and bolsters his students’ passion and interest in mathematics.