Chao Chen

Chao Chen is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State), where he has been since 2023. His research focuses on developing efficient algorithms for matrix computations, with applications ranging from solving partial differential equations to analyzing large high-dimensional datasets. Prior to this, he was a postdoctoral researcher at The University of Texas at Austin’s Oden Institute, working with George Biros and Gunnar Martinsson. He earned his PhD in Computational and Mathematical Engineering (ICME) from Stanford University in 2018, advised by Eric Darve. He also holds an MS from Stanford’s ICME (2014) and a BS in Information and Computational Mathematics from Nankai University (2012).

Education:

• PhD in Computational and Mathematical Engineering, Stanford University, 2018
• MS in Computational and Mathematical Engineering, Stanford University, 2014
• BS in Information and Computational Mathematics, Nankai University, 2012

Research Interests: Chen’s work emphasizes high-performance computing, numerical linear algebra, and randomized algorithms. He develops methods to reduce computational complexity and enhance performance on modern hardware. His applications span computational physics, data science, and machine learning. Notably, he has contributed to fast multipole methods, hierarchical solvers, and preconditioners for large-scale systems.

Teaching: Chen teaches courses such as Linear Analysis (MA 303), Mathematics of Scientific Computing (MA 402), Linear Transformations and Matrix Theory (MA 523), and Theory of Matrices (MA 723). His courses emphasize theoretical foundations and practical applications, often incorporating computational tools like MATLAB and LaTeX.

Presentations: He has presented at conferences worldwide, including SIAM Annual Meetings, Supercomputing Conferences, and specialized workshops on numerical linear algebra and fast solvers.