Research Interests

• High-dimensional and multiscale PDEs with applications to kinetic theory
• Scientific machine learning and structure-preserving low-rank tensor approximation
• High-order finite-difference, finite-volume, and finite-element discretizations
• High-performance scientific computing and software development

Education

• Ph.D. in Computational Mathematics, Science, and Engineering, Michigan State University, 2022
  • Advisor: Prof. Andrew J. Christlieb
• M.S. in Applied Mathematics, University of Akron, 2017
  • Advisor: Prof. Stefan Forcey
• B.S. in Applied Mathematics, University of Akron, 2017

Employment

• Postdoctoral Research Fellow, University of Delaware, Newark, DE, August 2023 - Present
  • Mentor: Prof. Jing-Mei Qiu
• Peter O’Donnell Jr. Postdoctoral Fellow, University of Texas at Austin, Austin, TX, August 2022 - August 2023
  • Mentors: Prof. Robert Moser and Prof. George Biros, PECOS
• Graduate Research Assistant, Michigan State University, East Lansing, MI, August 2017 - August 2022
• Los Alamos National Laboratory, Los Alamos, NM, May 2019 - August 2019
• Graduate Assistant, University of Akron, Akron, OH, August 2015 - May 2017

Awards

• Peter O’Donnell Jr. Postdoctoral Fellowship (The University of Texas at Austin)
• Ginther Graduate Fellowship (Michigan State University)
• Presidential Scholar (University of Akron)

Grants and Proposals

• PI: Predictive Ultrafast Low-Rank Simulations via Embedded Tensor Networks, Subaward from Lawrence Livermore National Laboratory, May 21, 2025 - March 22, 2026, Total: \($108,092\)
• PI: High-order Adaptive Rank Integrators for Multi-scale Kinetic Simulations : Algorithms, Analysis, and Integration with Novel Architectures, Air Force Office of Scientific Research, Computational Mathematics (white paper, submitted)
• Co-PI: Collaborative Research: RUI: Low-rank tensor methods with high-order accuracy and structure-preserving properties for kinetic simulations, National Science Foundation, Division of Mathematical Sciences (submitted)

Mentoring

• James Mou (undergraduate), University of Delaware, Summer 2024.
  • Focus: Low-rank tensor decompositions for PDEs and their acceleration on GPUs.

Service and leadership

• Co-organizer for the minisymposium on Low-rank Solvers with Applications to High-dimensional Problems at ICOSAHOM 2025, July 13-18, 2025
• Co-organizer, Numerical Analysis and PDE Seminar, University of Delaware, Fall 2024 - Spring 2025
• Graduate Representative, Coalition for National Science Funding advocacy meetings, Michigan State University, June 2021
• Organized the minisymposium on Fast Kernel Methods for Integral Equations at SIAM CSE `21, March 1-5, 2021

Programming and Software

• Languages: Python, MATLAB, Julia, C, C++, Fortran
• Libraries: BoBa, MFEM/PyMFEM, FFTW, NVIDIA GPU libraries (e.g., cuFFT, cuTensor, cuBLAS, cuSOLVER), Paraview, Qiskit
• Writing: LaTeX, Microsoft Office

Note: For a full CV, please contact me.