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Current courses (summer semester 2024)


Further courses

  • Current Topics in Stochastics (winter, annually)
  • Mathematics of Machine Learning (winter, annually)
  • Methods in Machine Learning (summer, annual)
  • Probabilistic Forecasting and Data Assimilation (summer, annual)
  • Uncertainty Quantification (winter, odd-numbered years)
  • Stochastic Methods for Material Science (winter, annual)
  • Experimental Design and Multivariate Statistics (summer, annual)


  • Uncertainty quantification for differential equations
  • High-dimensional approximation methods
  • Stochastic simulation methods, especially Markov chain Monte Carlo
  • Bayesian inference for inverse problems
  • Uncertainties in machine learning

Google Scholar Profile



Prefereed publications

  1. M. Hasenpflug, D. Rudolf, B. Sprungk (2024)
    Wasserstein convergence rates of increasingly concentrating probability measures.
    Ann. Appl. Probab. 34(3):3320-3347. [arXiv]
  2. H. Höllwarth, S. A.H. Sander, M. Werner, S. Fuhrmann, B. Sprungk (2023)
    Simulation of phase separation in Na2O-SiO2 glasses under uncertainty.
    Journal of Non-Crystalline Solids 621:122534 (7pp). 
  3. Lie, H. C., Rudolf, D., Sprungk, B., Sullivan T. J. (2023)
    Dimension-independent Markov chain Monte Carlo on the sphere.
    Scandinavian Journal of Statistics 50(4):1818-1858. [arXiv]
  4. Ernst, O. G., Pichler, A., Sprungk, B. (2022).
    Wasserstein sensitivity of Risk and Uncertainty Propagation.
    SIAM/ASA J. Uncertainty Quantification 10(3):915-948. [arXiv]
  5. Eigel, M., Ernst, O., Sprungk, B., Tamellini, L. (2022) 
    On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion.
    SIAM J. Numer. Anal. 60(2):659-687 [arXiv]
  6. Ernst, O. G., Sprungk, B., Tamellini, L. (2022). 
    On Expansions and Nodes for Sparse Grid Collocation of Lognormal Elliptic PDEs.
    In: H.-J. Bungartz et al. (Eds.) Sparse Grids and Applications - Munich 2018, Lecture Notes in Computational Science and Engineering, vol. 144, Springer Cham, pp. 1-31. [arXiv]
  7. Natarovskii, V., Rudolf, D., Sprungk, B. (2021)
    Geometric convergence of elliptical slice sampling.
    Proceedings of the 38th International Conference on Machine Learning, PLMR 139:7969-7978 [arXiv]
  8. Klebanov, I., Sprungk, B., Sullivan, T. J. (2021)
    The linear conditional expectation in Hilbert space.
    Bernoulli 27(4):2267-2299. [arXiv]
  9. Natarovskii, V., Rudolf, D., Sprungk, B. (2021)
    Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling.
    Ann. Appl. Probab. 31(2):806-825. [arXiv]
  10. Habeck, M., Rudolf, D., Sprungk, B. (2020)
    Stability of doubly-intractable distributions.
    Electron. Commun. Probab. 25, paper no. 62, 13pp. [arXiv]
  11. Schillings, C., Sprungk, B., Wacker, P. (2020)
    On the Convergence of the Laplace Approximation and Noise-Level Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems.
    Numerical Mathematics 145:915-971. [arXiv]
  12. Rudolf, D., Sprungk, B. (2020)
    On a Metropolis-Hastings importance sampling estimator
    Electron. J. Statist. 14(1):857-889. [arXiv]
  13. Sprungk, B. (2020)
    On the Local Lipschitz Robustness of Bayesian Inverse Problems.
    Inverse Problems 36:055015 (31pp). [arXiv]
  14. Ernst, O. G., Sprungk, B., Tamellini, L. (2018)
    Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables.
    SIAM J. Numer. Anal. 56(2):877-905. [arXiv]
  15. Rudolf, D., Sprungk, B. (2018)
    On a Generalisation of the Preconditioned Crank-Nicolson Metropolis Algorithm.
    Found. Comput. Math. 18:309-343. [arXiv]
  16. Rudolf, D., Sprungk, B. (2017)
    Metropolis-Hastings Importance Sampling Estimator.
    Proc. Appl. Math. Mech. 17:731-734.
  17. Hundt, S., Sprungk, B., Horsch, A. (2017)
    The Information Content of Credit Ratings: Evidence from European Convertible Bond Markets.
    The European Journal of Finance 23(14):1414-1445.
  18. Ernst, O. G., Sprungk, B., Starkloff, H.-J. (2015)
    Analysis of the ensemble and polynomial chaos Kalman filters in Bayesian inverse problems.
    SIAM/ASA J. Uncertainty Quantification 3(1):823-851. [arXiv]
  19. Ernst, O. G., Sprungk, B., Starkloff, H.-J. (2014)
    Bayesian inverse problems and Kalman filters.
    In: Dahlke S. et al. (Eds.) Extraction of Quantifiable Information from Complex Systems, Lecture Notes in Computational Science and Engineering, Vol. 102, Springer, Cham, pp. 133-159.
  20. Ernst, O. G., Sprungk, B. (2014)
    Stochastic collocation for elliptic PDEs with random data - the lognormal case.
    In: J. Garcke and D. Pflüger (Eds.) Sparse Grids and Applications - Munich 2012, Lecture Notes in Computational Science and Engineering, Vol. 97, Springer, Cham, pp. 29-53.
  21. Sprungk, B., van den Boogaart, K. G. (2013)
    Stochastic differential equations with fuzzy drift and diffusion.
    Fuzzy Sets and Systems 230(1):53-64.

Since 02/2024W2-Professor for Applied Mathematics, Institute for Stochastics, TU Bergakademie Freiberg
02/2020 - 01/2024Tenure-Track Professor for Applied Mathematics, Faculty of Mathematics and Computer Science, TU Bergakademie Freiberg
04/2018 - 01/2020Postdoc, Institute for Mathematical Stochastics, Georg-August University Göttingen
08/2017 - 03/2018Postdoc, DFG Research Training Group 1953 "Statistical Modelling of Complex Systems and Processes", University of Mannheim
06/2017PhD in Mathematics, Thesis "Numerical Methods for Bayesian Inference in Hilbert Spaces", TU Chemnitz
05/2013 - 08/2017Doctoral student, Professorship of Numerical Mathematics, TU Chemnitz
04/2011 - 04/2013Research Assistant, DFG Priority Programme 1324 "Extraction of Quantifiable Information from Complex Systems", Institute for Numerical Mathematics and Optimisation, TU Bergakademie Freiberg
07/2009 - 12/2009Semester abroad, Norway University of Science and Technology, Trondheim
10/2005 - 03/2011Diploma Programme Applied Mathematics, TU Bergakademie Freiberg
1985Born in Possendorf (Saxony)