Chao Chen
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Assistant Professor Contact: chao_chen@ncsu.edu |
About me
Before joining NC State, Chao Chen was a postdoctoral researcher at the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin, working with George Biros and Per-Gunnar Martinsson. He received his PhD in 2018 Stanford University's Institute for Computational and Mathematical Engineering (ICME), advised by Eric Darve.
Chao has held research internships at NVIDIA Research (2018), Sandia National Laboratories’ Center for Computing Research (2015-2017), and Lawrence Livermore National Laboratory's Computational Materials Science Group (2013).
Research Vision
The central theme of my research is the development of robust, scalable, and provable numerical algorithms that overcome the “curse of dimensionality” in modern scientific computing. As we transition into the exascale era, the primary bottlenecks in simulation and discovery are no longer just floating-point operations, but the massive communication costs and super-linear scaling inherent in high-dimensional operators. My research program addresses these challenges through three integrated pillars:
Hierarchical Compression: I seek to unify Hierarchical-matrix (H-matrix) theory with Tensor Decompositions to create “full-stack” compression frameworks. By exploiting the low-rank structure of Green's functions and high-dimensional kernel matrices, we attain O(N) operations for systems that were previously computationally intractable. This work can provide the mathematical backbone for operator learning, replacing black-box neural networks with transparent, physics-respecting approximations.
Trustworthy Randomized NLA (RandNLA): Reliability is paramount in scientific discovery. My work in Randomized Numerical Linear Algebra focuses on moving beyond empirical speedups toward provable stability. By developing adaptive sketching and pivoting methods, we ensure that randomized solvers maintain the same precision as their deterministic counterparts while providing the massive speedups.
Hardware-Software Co-Design for Exascale Physics: Theoretical breakthroughs must translate to wall-clock performance. Leveraging my experiences at Sandia and LLNL, my group develops communication-avoiding algorithms optimized for GPU-accelerated architectures. We apply these kernels to challenging problems, such as the nonlinear Stokes equations in ice sheet modeling and dislocation dynamics. Our mathematical advances solve some of the most pressing physical modeling barriers.
Recent News
[May 2026] Co-organized a Minisymposium “Hierarchical Low-Rank Approximations: Algorithms and Applications” with Arvind Saibaba at 27th Conference of the International Linear Algebra Society (ILAS 2026).
[Apr 2026] Talk at Applied Math Seminar (online) at University of Arkansas (upcoming).
[Mar 2026] Co-organized a Minisymposium “Low-rank Approximation with Applications to Hierarchical Matrices and Tensors” with Yang Liu at SIAM Conference on Parallel Processing for Scientific Computing (PP26).
[Nov 2025] New work “Parametric Hierarchical Matrix Approximations to Kernel Matrices” is available arXiv.
[Sep 2025] New work “Parallel GPU-Accelerated Randomized Construction of Approximate Cholesky Preconditioners” is available arXiv.