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Daniel Sanz Alonso

Contact: Office: B3.04. Email: d.sanz-alonso{at}warwick.ac.uk.

CV (PDF Document)

Research

My research is in inverse problems, nonlinear filtering, data assimilation, uncertainty quantification and computational statistics. I have worked in three main topics:

  • Filtering chaotic dynamical systems:
    • D. Sanz-Alonso, A. M. Stuart. Long-time asymptotics of the filtering distribution for partially observed chaotic dynamical systems. SIAM/ASA J. Uncertainty Quantification, 3(1), 1200-1220 (2015). Available here.
    • K. J. H. Law, D. Sanz-Alonso, A. Shukla, A. M. Stuart. Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators. Physica D: Nonlinear Phenomena, 325, 1-13 (2016). Available here.
  • Importance sampling, inverse problems and filtering in high dimensions and small noise regimes:
    • S. Agapiou, O. Papaspiliopoulos, D. Sanz-Alonso, A. M. Stuart. Importance sampling: Computational complexity and intrinsic dimension. Submitted. Available as arXiv:1511.06196 (2015).
  • Gaussian process approximation of SDEs.
    • D. Sanz-Alonso and A. M. Stuart. Gaussian approximations of small noise diffusions in Kullback-Leibler divergence. Submitted. Available as arXiv:1605.05878 (2016).

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