Research

My research is broadly in the field of mathematical data science. More specifically, I am interested in developing robust iterative algorithms for solving problems involving high-dimensional or multimodal objects like tensors. To tackle these problems, I utilize tools from numerical linear algebra, high-dimensional probability, machine learning, statistics, and optimization.

Publications

• On Trimming Tensor-structured Measurements and Efficient Low-rank}
  (by Suryanarayanan S and Rebrova E.)
  arXiv:2502.02843, 2025.

• Randomized Kaczmarz methods for t-product tensor linear systems with factorized operators
  (by Castillo A, Haddock J, Hartsock I, Hoyos P, Kassab L, Kryshchenko A, Larripa K, Needell D, Suryanarayanan S, Yacoubou Djima K.)
  BIT Numerical Mathematics, 2025.

• Quantile-Based Randomized Kaczmarz for Corrupted Tensor Linear Systems
  (by Castillo A, Haddock J, Hartsock I, Hoyos P, Kassab L, Kryshchenko A, Larripa K, Needell D, Suryanarayanan S, Yacoubou Djima K.)
  arXiv:2503.18190, 2025.

• Block Gauss-Seidel methods for t-product tensor regression
  (by Castillo A, Haddock J, Hartsock I, Hoyos P, Kassab L, Kryshchenko A, Larripa K, Needell D, Suryanarayanan S, Yacoubou Djima K.)
  Numerical Algorithms, 2025.