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My research interests include: (1) High-performance computing: high-order schemes (finite difference/volume/element), particle-in-cell, scalable iterative solvers, preconditioning, adaptive mesh refinement, GPU accelerations; (2) Scientific computing and simulations: multiscale and multiphysics problems, fusion modeling, computational plasma physics; (3) AI/ML and data science: structure-preserving ML, dynamical system learning, scale bridging, ML-based surrogates, denoising, Bayesian inference
I teach applied mathematics and computational science with an emphasis on connecting theory, algorithms, and real scientific applications. My courses integrate concrete examples, prototype implementations, and project-based learning to link numerical linear algebra, PDEs, and parallel computing with HPC, AI/ML, and physics. I recently taught Numerical Linear Algebra, where I connect core topics such as conditioning, QR, SVD, and eigenvalue problems to data analysis and scientific machine learning, emphasizing both mathematical insight and computational consequences. In my High-Performance Scientific Computing course I design structured team projects using professional research workflows and actively incorporate AI-assisted development to train students to use emerging tools critically and responsibly.
1. E. Drimalas, F. Fraschetti, C.-K. Huang, and Q. Tang. Symplectic neural network and its applications to charged particle dynamics in electromagnetic fields, Physics of Plasmas, 2025.
2. S.-H. Wang, Y. Huang, J. Baker, Y.-E. Sun, Q. Tang, and B. Wang. A theoretically-principled sparse, connected, and rigid graph representation of molecules, ICLR, 2025.
3. D. Serino, Q. Tang, X.-Z. Tang, T. Kolev, and K. Lipnikov. An adaptive Newton-based free-boundary Grad–Shafranov solver, SIAM Journal on Scientific Computing, 2025.
4. G. Wimmer, B. Southworth, and Q. Tang. A structure-preserving discontinuous Galerkin scheme for the Cahn-Hilliard equation including time adaptivity, Journal of Computational Physics, 2025.
5. D. Serino, A. Alvarez Loya, J. Burby, I. Kevrekidis, and Q. Tang. Fast-slow neural networks for learning singularly perturbed dynamical systems, Journal of Computational Physics, 2025.