Dedicated researcher and educator specializing in Computational Mathematics, Deep Learning, and Numerical Analysis of Nonlocal PDEs. Committed to advancing computational methods and applying them to neuroscience and other scientific domains.
I am a computational mathematician with expertise in numerical methods for partial differential equations, deep learning, and high-performance computing. My research bridges the gap between theoretical mathematics and practical applications in scientific computing and neuroscience.
My work focuses on developing and analyzing numerical methods for nonlocal problems, stochastic PDEs, and computational neuroscience applications. I am particularly interested in the intersection of traditional numerical methods and modern machine learning techniques.
In my teaching, I emphasize the connections between mathematical theory and computational implementation, helping students develop both theoretical understanding and practical coding skills for solving real-world problems.
Ph.D. in Computational Science
Florida State University, 2016
Advisor: Professor Max Gunzburger
M.Sc. in Computational Mathematics
Jilin University, 2011
Advisor: Professor Yongkui Zou
B.Sc. in Computational Mathematics
Jilin University, 2009
Assistant Professor
University of Southern Mississippi, 2022-present
Research Assistant Professor
Temple University, 2019-2022
Visiting Assistant Professor
Missouri University of Science and Technology, 2018-2019
Download my complete academic CV for details on my research, publications, and professional experience.
Download CV (PDF)My research focuses on developing computational methods for solving complex mathematical problems with applications in science and engineering. I combine traditional numerical analysis with modern computational approaches to tackle challenging problems.
Developing computational models for calcium dynamics in neurons and using deep learning to model ion channel behavior.
Investigating the theoretical foundations of deep neural networks and developing hybrid PDE-deep learning models for scientific applications.
Designing and analyzing finite element methods (FEM, Weak Galerkin FEM, VEM) for nonlocal and stochastic partial differential equations.
A selection of my most significant research contributions in peer-reviewed journals and conference proceedings.
I have developed and taught courses at all levels, from introductory undergraduate to advanced graduate seminars, with emphasis on computational mathematics and its applications.
My approach emphasizes the integration of mathematical theory with computational implementation. I believe students learn best when they understand both the "why" and the "how" of mathematical concepts.
I create an environment where students can explore mathematical concepts through coding and visualization, making abstract ideas more concrete and applicable.
My goal is to develop students' computational thinking skills that will serve them in both academic and industry careers in the increasingly data-driven world.
My courses typically cover:
I incorporate hands-on coding exercises using Python, MATLAB, and other computational tools to reinforce theoretical concepts.
I welcome inquiries regarding research collaborations, speaking engagements, and academic opportunities.
Contact Information
Email: qingguang.guan@usm.edu
Phone: +1-601-266-4300
Office:
School of Mathematics and Natural Sciences
University of Southern Mississippi
118 College Drive
Hattiesburg, MS 39406