Dr Aretha Teckentrup

Lecturer in Mathematics of Data Science
Office: JCMB 5409
Phone: +44 (0)13165 05776
Email: a.teckentrup[at]

Postal address: School of Mathematics, University of Edinburgh, James Clerk Maxwell Building, Edinburgh, EH9 3FD

About me:

I joined the University of Edinburgh in October 2016. Before this, I worked as a postdoctoral research associate with Andrew Stuart at the University of Warwick, and with Max Gunzburger at Florida State University. I obtained my PhD at the University of Bath in 2013, supervised by Rob Scheichl.

A more detailed CV can be found here (last updated February 2019).

Research interests:

- Uncertainty quantification
- Bayesian inference for computationally intensive forward models
- Numerical analysis of Gaussian process surrogate models in uncertainty quantification
- Development and analysis of multilevel sampling methods
- Numerical analysis of partial differential equations with random input data
- Multivariate interpolation and integration


- M. Park, A.L. Teckentrup. Improved Multilevel Monte Carlo Methods for Finite Volume Discretisations of Darcy Flow in Randomly Layered Media. Submitted, 15 June 2015. Available as arXiv preprint arXiv:1506.04694.

- M. Gunzburger, A.L. Teckentrup. Computing Approximate Optimal Point Sets for Total Degree Polynomial Interpolation in Moderate Dimensions. Submitted, 11 July 2014. Available as arXiv preprint arXiv:1407.3291. Interpolation points computed in this paper are available in the MATLAB scripts points_cube.m (for the unit cube) and points_ball.m (for the unit ball).


- H.C. Lie, T.J. Sullivan, A.L. Teckentrup. Random forward models and log-likelihoods in Bayesian inverse problems. SIAM/ASA Journal on Uncertainty Quantification, 6(4), 1600-1629, 2018. Available as arXiv preprint arXiv:1712.05717.

- M.M. Dunlop, M. Girolami, A.M. Stuart, A.L. Teckentrup. How deep are deep Gaussian processes? Journal of Machine Learning Research, 19, 1-46, 2018. Available as arXiv preprint arXiv:1711.11280.

- A.M. Stuart, A.L. Teckentrup. Posterior Consistency for Gaussian Process Approximations of Bayesian Posterior Distributions. Mathematics of Computation, (87), 721-753, 2018. Available as arXiv preprint arXiv:1603.02004.

- R. Scheichl, A.M. Stuart, A.L. Teckentrup. Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 493–518, 2017. Available as arXiv preprint arXiv:1602.04704.

- A.L. Teckentrup, P. Jantsch, C.G. Webster, M. Gunzburger. A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM/ASA Journal on Uncertainty Quantification, 3(1), 1046–1074, 2015. Available as arXiv preprint arXiv:1404.2647.

- T.J. Dodwell, C. Ketelsen, R. Scheichl, A.L. Teckentrup. A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow. SIAM/ASA Journal on Uncertainty Quantification 3(1), 1075-1108, 2015. Available as arXiv preprint arXiv:1303:7343.

- A.L. Teckentrup, R. Scheichl, M.B. Giles, E. Ullmann. Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients. Numerische Mathematik, 125(3), 569-600, 2013. Available as arXiv preprint arXiv:1204:3476.

- J. Charrier, R.Scheichl, A.L. Teckentrup. Finite Element Error Analysis of Elliptic PDEs with Random Coefficients and its Application to Multilevel Monte Carlo Methods. SIAM Journal on Numerical Analysis, 51(1), 322-352, 2013. BICS preprint 2/11, available here.

- A.L. Teckentrup. Multilevel Monte Carlo methods for highly heterogeneous media. Proceedings of the Winter Simulation Conference 2012. Available at Available as arXiv preprint arXiv:1206:1479.

- K.A. Cliffe, M.B. Giles, R. Scheichl, A.L. Teckentrup. Multilevel Monte Carlo Methods and Applications to Elliptic PDEs with Random Coefficients. Computing and Visualization in Science, 14(1), 3-15, 2011. BICS preprint 1/11, available here.


- A.L. Teckentrup. Multilevel Monte Carlo methods and uncertainty quantification. PhD thesis, University of Bath (UK), June 2013. PDF available online here.

Upcoming events and conferences:

- Oberwolfach workshop on Uncertainty Quantification, Oberwolfach (Germany), March 10 - 16, 2019

- Program on Data Assimilation: Theory, Algorithms and Applications, CRM (Canada), May 1- 31, 2019

- SIAM UKIE National Student Chapter Conference, Manchester (UK), June 10 - 11, 2019

- Model-oriented Data Analysis and Optimum Design, Smolenice castle (Slovakia), June 23-28, 2019

- Oberwolfach workshop on Computational Multiscale Methods, Oberwolfach (Germany), July 28 - August 3, 2019