I am Assistant Professor in Predictive Modelling in the Mathematics Institute and School of Engineering at the University of Warwick and Research Group Leader for Uncertainty Quantification at the Zuse Institute Berlin. I have wide interests in uncertainty quantification the broad sense, understood as the meeting point of numerical analysis, applied probability and statistics, and scientific computation. On this site you will find information about how to contact me, my research, publications, and teaching activities.
Like many international conferences, the SIAM Conference on Uncertainty Quantification planned for 24–27 March 2020 had to be postponed indefinitely in view of the Covid-19 pandemic. Undeterred by this, the speakers of four minisymposia on the theme of Probabilistic Numerical Methods have generously taken the time to adapt their talks for a new medium and record them for general distribution. The talks can be found at http://probabilistic-numerics.org/meetings/SIAMUQ2020/.
We hope that these talks will be of general interest. Furthermore, the speakers have declared themselves ready to answer questions in written form. If you would like to ask any questions or contribute to the discussion, then please submit your question via this form by 10 May 2020.
There is an opening for a PhD student to work with me and co-PIs Jon Cockayne and James Kermode on the project “Adaptive probabilistic meshless methods for evolutionary systems” as part of the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems at the University of Warwick.
This project will develop and implement a new class of numerical solvers for evolving systems such as interacting fluid-structure flows. To cope with extreme strain rates and large deformations these new solvers will be adaptive and meshless, and they will also implicitly represent their own solution uncertainty, thus enabling optimal design and uncertainty quantification. This exciting project brings together aspects of continuum mechanics, numerical methods for partial differential equations, and statistical machine learning.
Interested students should contact me and the other PIs with informal queries. Formal applications should use the HetSys application page.
It is a pleasure to announce that I have accepted an Assistant Professorship in Predictive Modelling at the University of Warwick, to be held jointly between the Mathematics Institute and the School of Engineering.
This position will also involve collaborative work in a number of interdisciplinary research centres and centres for doctoral training, in particular the Warwick Centre for Predictive Modelling and the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems.
It is a pleasure to announce that Birzhan Ayanbayev will join the UQ research group as a postdoctoral researcher with effect from 28 February 2020. He will be working on the DFG-funded project “Analysis of maximum a posteriori estimators: Common convergence theories for Bayesian and variational inverse problems”.
The article “Geodesic analysis in Kendall's shape space with epidemiological applications” by Esfandiar Nava-Yazdani, Christoph von Tycowicz, Christian Hege, and myself has just appeared online in the Journal of Mathematical Imaging and Vision.
E. Nava-Yazdani, H.-C. Hege, T. J. Sullivan, and C. von Tycowicz. “Geodesic analysis in Kendall's shape space with epidemiological applications.” Journal of Mathematical Imaging and Vision 62(4):549–559, 2020.
Abstract. We analytically determine Jacobi fields and parallel transports and compute geodesic regression in Kendall’s shape space. Using the derived expressions, we can fully leverage the geometry via Riemannian optimization and thereby reduce the computational expense by several orders of magnitude over common, nonlinear constrained approaches. The methodology is demonstrated by performing a longitudinal statistical analysis of epidemiological shape data. As an example application, we have chosen 3D shapes of knee bones, reconstructed from image data of the Osteoarthritis Initiative. Comparing subject groups with incident and developing osteoarthritis versus normal controls, we find clear differences in the temporal development of femur shapes. This paves the way for early prediction of incident knee osteoarthritis, using geometry data alone.