Guannan Zhang
About Guannan Zhang
Guannan Zhang is a Senior Research Staff member at Oak Ridge National Laboratory, where he has worked since 2020. He holds a PhD in Computational and Applied Mathematics from Florida State University and has extensive research interests in high-dimensional approximation and machine learning, among other areas.
Current Position at Oak Ridge National Laboratory
Guannan Zhang currently holds the position of Senior Research Staff at Oak Ridge National Laboratory. He has been in this role since 2020, contributing to various research initiatives in Oak Ridge, Tennessee. His work focuses on advancing methodologies in computational mathematics and related fields.
Previous Experience at Oak Ridge National Laboratory
Prior to his current role, Guannan Zhang worked at Oak Ridge National Laboratory as Research Staff from 2014 to 2020. He also served as a Distinguished Staff Fellow, known as Householder Fellow, from 2012 to 2014. His extensive experience at this institution has shaped his research capabilities and expertise.
Educational Background
Guannan Zhang has a robust educational background in mathematics and computational sciences. He completed his Bachelor's degree in Mathematics at Shandong University from 2003 to 2007. He further pursued a Master of Science in Mathematics at the same university from 2007 to 2009. Subsequently, he earned a Master of Science in Computational and Applied Mathematics from Florida State University from 2009 to 2011, followed by a Doctor of Philosophy in the same field from 2009 to 2012.
Joint Faculty Appointment
Since 2014, Guannan Zhang has held a joint faculty appointment with the Department of Mathematics and Statistics at Auburn University. This role allows him to engage in academic collaboration and contribute to the development of mathematical research and education.
Research Interests
Guannan Zhang's research interests encompass a range of advanced topics in mathematics and computational science. His areas of focus include high-dimensional approximation, uncertainty quantification, machine learning and artificial intelligence, stochastic optimization and control, numerical solutions of stochastic differential equations, and model reduction for parametrized differential equations.