I am an Assistant Professor in the Department of Mathematics at Syracuse University. Before joining Syracuse, I was a Visiting Assistant Professor at the University of Wisconsin–Madison (2020–2024). I received my PhD in Applied Mathematics from the University of Delaware.

My research is largely motivated by the observation that diverse problems in science and engineering often share common mathematical structures. I am interested in identifying and understanding these structures through computational tools. I value both mathematical rigor and practical utility, and hold the belief that they are complementary. My current research interests focus on high-order numerical methods for high-dimensional and multiscale systems, data-driven and machine learning acceleration of principle-based numerical approaches, and applications such as electromagnetic waves, radiative transfer, and inverse problems.

News

• [Jan 2026] New paper: J. L. Torchinsky, S. Du, and S. N. Stechmann. Angular-spatial hp-adaptivity for radiative transfer with discontinuous Galerkin spectral element methods. J. Quant. Spectrosc. Radiat. Transf. 348 (2026). DOI
• [Jan 2026] Submitted: S. Du, T. Le, S. N. Stechmann. Element learning with hp-adaptivity: machine learning-based acceleration of hp-adaptive spectral element methods, and application to atmospheric radiative transfer.
• [Nov 2025] Gave a talk at SIAM New York–New Jersey–Pennsylvania Section: "Inverse radiative transfer via goal-oriented adaptive mesh refinement."
• [Nov 2025] Submitted: B. Cockburn, S. Du, M. A. Sánchez. A spectral analysis of hp-hybridized mixed and HDG methods for parametric second-order elliptic problems.
• [Oct 2025] New paper: D. H. Marsico, S. Du, and S. N. Stechmann. Can second-order numerical accuracy be achieved for moist atmospheric dynamics with non-smoothness at cloud edge? J. Adv. Model. Earth Syst. 17 (2025). DOI
• [Jul 2025] Gave a talk at the Third HKSIAM Biennial Conference, Chinese University of Hong Kong: "Accelerating finite element-type methods with machine learning on substructures."
• [Mar 2025] Gave a talk at SIAM Sectional Meeting, Fort Worth, TX: "Element learning: accelerating finite element-type methods via machine learning, with applications to radiative transfer."

Research keywords

• Finite element and discontinuous Galerkin methods
• Scientific machine learning and data-driven methods
• Computational inverse and ill-posed problems
• Numerical methods for radiative transfer
• Electromagnetic and elastic/viscoelastic waves

Visit the Research page for more information.