Chao Chen
Bio
Chao Chen has been an assistant professor in the Department of Mathematics at NC State University since 2023. Before that, he was a postdoc in the Oden Institute at The University of Texas at Austin. He was an intern at Nvidia Research in summer 2018, an intern at Center for Computing Research in Sandia National Laboratories for three summers (2015-2017) and an intern at Computational Materials Science Group in Lawrence Livermore National Laboratory in summer 2013.
Education
Ph.D. Computational and Mathematical Engineering Stanford University 2018
M.S. Computational and Mathematical Engineering Stanford University 2014
B.S. Information and Computational Mathematics Nankai University 2014
Area(s) of Expertise
Chao’s research generally focuses on developing efficient algorithms for matrix computations with applications to computational tasks ranging from solving partial differential equations to analyzing large high-dimensional datasets.
Publications
- Adaptive Parallelizable Algorithms for Interpolative Decompositions via Partially Pivoted LU , Numerical Linear Algebra with Applications (2025)
- Parallel GPU-Accelerated Randomized Construction of Approximate Cholesky Preconditioners , ArXiv (2025)
- Robust Blockwise Random Pivoting: Fast and Accurate Adaptive Interpolative Decomposition , SIAM Journal on Matrix Analysis and Applications (2025)
- Scalable KNN Graph Construction for Heterogeneous Architectures , ACM Transactions on Parallel Computing (2025)
- A simplified fast multipole method based on strong recursive skeletonization , Journal of Computational Physics (2024)
- An O(N) distributed-memory parallel direct solver for planar integral equations , PROCEEDINGS 2024 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM, IPDPS 2024 (2024)
- Efficient algorithms for computing rank‐revealing factorizations on a GPU , Numerical Linear Algebra with Applications (2023)
- Overlapping Domain Decomposition Preconditioner for Integral Equations , SIAM Journal on Scientific Computing (2022)
- Solving Linear Systems on a GPU with Hierarchically Off-Diagonal Low-Rank Approximations , SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (2022)
- Fast Approximation of the Gauss--Newton Hessian Matrix for the Multilayer Perceptron , SIAM Journal on Matrix Analysis and Applications (2021)