Statistical Science


Dr Dimitra Salmanidou

PositionSenior Research Fellow
Phone (external)+44 (0)20 3108 3227
Phone (internal)53227
ThemesComputational Statistics

* @ucl.ac.uk

Biographical Details

Dimitra is a Senior Research Fellow in the Statistical Science Department and a visiting researcher at the Alan Turing Institute, affiliated to the data-centric engineering programme.

Dimitra has been working as a postdoctoral researcher at UCL since 2017, after obtaining her PhD in Applied and Computational Mathematics (University College Dublin, Ireland). She is currently a Co-I in the project "Future Indonesian Tsunamis: Towards End-to-end Risk quantification (FITTER)" where she leads the Work Package: “Bathymetry and Source Parameterisations”.

Dimitra’s research focuses on earthquake and landslide induced tsunami hazard through high-resolution numerical modelling and implementation of state-of-the-art methods for uncertainty quantification (UQ), such as Gaussian Process Emulation, Bayesian inference and adaptive experimental design. She also works on tsunami evacuation case studies with the aid of agent-based models (ABMs).

Research Interests

tsunami modelling, uncertainty quantification, sensitivity analysis, Bayesian calibration, statistical emulation

Selected publications

  • Salmanidou D.M., Heidarzadeh M., Guillas S. (2019). Probabilistic landslide-generated tsunamis in the Indus Canyon, NW, Indian Ocean, using statistical emulation. Pure and Applied Geophysics, https://doi.org/10.1007/s00024-019-02187-3
  • Salmanidou D.M., Georgiopoulou A., Guillas S., Dias F. (2018). Rheological considerations for the modelling of submarine sliding at the Rockall Bank, NE Atlantic Ocean, Physics of Fluids, 30: 030705, https://doi.org/10.1063/1.5009552
  • Salmanidou D.M., Guillas S., Georgiopoulou A., Dias F. (2017). Statistical emulation of landslide induced tsunamis at the Rockall Bank, NE Atlantic Ocean. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473: 20170026, https://doi.org/10.1098/rspa.2017.0026