Randomized numerical linear algebra: tensor data analysis, dimensionality reduction, applied probability.
Publications and Preprints
Sunlayer: Stable denoising with generative networks.
by R. Jin, D. G. Mixon and S. Villar. Submitted ,2026.
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Data selection: at the interface of PDE-based inverse problem and randomized linear algebra.
by K. Hellmuth, R. Jin, Q. Li and S. Wright. To appear in "Handbook of Numerical Analysis".
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Continuous nonlinear adaptive experimental design via gradient flow.
by R. Jin, Q. Li, S. Mussmann and S. Wright. 2024.
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Unique reconstruction for discretized inverse problems: a random sketching approach.
by R. Jin, Q. Li, A. Nair and S. Stechmann. Inverse Problems, 2025.
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Optimal experimental design via gradient flow.
by R. Jin, M. Guerra, Q. Li and S. Wright. To appear in "Communications on Pure and Applied Analysis".
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Scalable symmetric Tucker tensor decomposition.
by R. Jin, J. Kileel, T. G. Kolda and R. Ward. SIAM Journal on Matrix Analysis and Applications, 2024.
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Space-time reduced-order modeling for uncertainty quantification.
by R. Jin, F. Rizzi and E. Parish. CSRI Summer Proceedings, Sandia National Laboratories, 2021.
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Tensor-structured sketching for constrained least squares.
by K. Chen and R. Jin. SIAM Journal on Matrix Analysis and Applications, 2021.
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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]