Ruhui Jin

Research interests

I work on mathematics of data science and data-driven tools for learning physical systems. My research can be characterized into two themes:
• Computational inverse problems: PDE-based optimization, experimental design, scientific machine learning.

• Randomized numerical linear algebra: tensor data analysis, dimensionality reduction, applied probability.

Publications and Preprints

• Continuous nonlinear adaptive experimental design via gradient flow.
  by R. Jin, Q. Li, S. Mussmann and S. Wright. Submitted, 2024.
  [PDF]
• Unique reconstruction for discretized inverse problems: a random sketching approach.
  by R. Jin, Q. Li, A. Nair and S. Stechmann. Inverse Problems, 2025.
  [PDF] [DOI]
• Optimal experimental design via gradient flow.
  by R. Jin, M. Guerra, Q. Li and S. Wright. 2024.
  [PDF]
• Scalable symmetric Tucker tensor decomposition.
  by R. Jin, J. Kileel, T. G. Kolda and R. Ward. SIAM Journal on Matrix Analysis and Applications, 2024.
  [PDF] [DOI]
• Space-time reduced-order modeling for uncertainty quantification.
  by R. Jin, F. Rizzi and E. Parish. CSRI Summer Proceedings, Sandia National Laboratories, 2021.
  [PDF] [DOI]
• Tensor-structured sketching for constrained least squares.
  by K. Chen and R. Jin. SIAM Journal on Matrix Analysis and Applications, 2021.
  [PDF] [DOI]
• Faster Johnson-Lindenstrauss Transform via Kronecker Products.
  by R. Jin, T. G. Kolda and R. Ward. Information and Inference: A Journal of the IMA, 2020.
  [PDF] [DOI]