Welcome to my personal website! I am a postdoctoral researcher and lecturer at the University of Potsdam Institute for Mathematics and an associate member of the Collaborative Research Centre SFB 1294 on data assimilation. I am supervised by professor Melina Freitag. Prior to this, I was a PhD candidate at Cornell University’s Center for Applied Mathematics supervised by professor Anil Damle.

I design and analyze numerical algorithms that address core mathematical challenges in uncertainty quantification for models of high-dimensional dynamical systems, particularly Earth systems such as the ocean or atmosphere. I focus primarily on resolving computational challenges in data assimilation that arise when the model size strains computational resources, or becomes too large to be constrained by the available data. My work in this vein draws on techniques involving dimensionality reduction, physics-informed regularization, data-sparse matrix computations, and low-rank approximation. Because of the central role that matrix computations play in my work, much of my research has also focused on designing and analyzing efficient computational primitives for numerical linear algebra.

Publications and Preprints

• R. Armstrong and A. Damle, “Collect, Commit, Expand: Efficient CPQR-Based Column Selection for Extremely Wide Matrices,” SIAM Journal on Scientific Computing, 2026, 48 (4), A1951-A1978 [SIAM online] [arXiv].

• R. Armstrong and I. Grooms, “Data Assimilation With An Integral-Form Ensemble Square-Root Filter,” Journal of Computational Physics, 2025, 543, 114413 [JCP online] [arXiv].

• R. Armstrong, A. Buzali, and A. Damle, “Structure-Aware Analyses and Algorithms for Interpolative Decompositions,” SIAM Journal on Scientific Computing, 2025, 47 (3), A1527-A1554 [SIAM online] [arXiv].