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Dan Crisan

 
 

Dan Crisan- Imperial College London. UK

Dan Crisan is a Professor of Mathematics at Imperial College London. After obtaining his PhD at the University of Edinburgh, Crisan first went to Imperial in 1995 as a postdoctoral fellow. After a brief spell at the University of Cambridge, Crisan returned to Imperial in 2000, where he was awarded a Governors' Lectureship. His research is in the broad area of Stochastic Analysis and Applied Probability. Crisan has authored over 70 publications in his field. He wrote a book on stochastic filtering published by Springer Verlag and co-edited a major Oxford University Press monograph on Nonlinear Filtering. Dan Crisan is the Director of the Centre for Doctoral Training in the Mathematics for Planet Earth (www.mpecdt.org). For his work in establishing the Centre, Crisan was awarded the 2018 Imperial College President's Award for Excellence in Research Supervision. Crisan is one of the founding editors of the series of Springer Briefs in the Mathematics of Planet Earth. Weather, Climate and Oceans (http://www.springer.com/series/15250).

Research stay at UC3M: DEPARTMENT OF SIGNAL THEORY AND COMMUNICATIONS

Project:

Particle Filters for high-dimensional problems

The threat of human caused climate warming and its accompanying extreme weather variability are undeniable. Dealing with this threat has become one of the central scientific and engineering challenges of our time. As the bounds of hardware capabilities are reached, the efficiency of the methodologies used in numerical weather prediction becomes the overriding objective. The overarching goal of the proposed research is to develop theoretically justified computational methods for statistical inference in high-dimensional or large data problems. The research will deliver new algorithms, termed particle filters, with supporting mathematical theory to be applied in numerical weather prediction. The three key strands of the research are:

  1. To systematically analyse Particle Filters embedded in suitably chosen topological spaces.
  2. To design new Particle Filtering approaches for explaining and quantifying the uncertainty of high dimensional models.
  3. To develop Particle Filters able to handle multiscale interaction in a nested hierarchy of models from coarse to fine scale.
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