Senior LecturerPhilipp Wacker

Jack Erskine 613
Internal Phone: 92359
Strongly interested in good communication of math&stats, and in how we can improve learning and scientific collaboration. He/him.

Research Interests

If you are a student interested in finding a project (Summer project, a thesis project, or a PhD project), or if you are a researcher interested in scientific collaboration, see my research webpage (link at the very end of this page). Feel free to write me an email or drop by my office if you'd like to discuss options!

I am interested in most mathematical aspects involving "getting information about hidden parameters via indirect and noisy observation". Different communities have different names for that, some of which are "(Nonparametric) Statistics", "Inverse Problems", "Bayesian statistics", "Inference", "Regression", "Data Assimilation", "(semi-)Supervised Learning".

These aspects include
• the question "can this work at all?" (i.e. well-posedness of the inversion process),
• Computational issues, algorithmic advances, and efficient implementation of inversion, which is often related to
• Useful mathematical/conceptual approximations (linearity/Gaussianity assumptions, e.g. within the Ensemble Kalman methodology; or the Laplace approximation as a surrogate measure for use in computational models) which make the problem computationally more feasible and raises the question of "how rough is this approximation?", which is a key question of
• Error analysis, convergence behaviour and stability of inversion schemes; as well as
• difficult mathematical questions related to probability theory, functional analysis (in particular for Banach-space-valued inverse problems)

Recent Publications

  • Bungert L. and Wacker P. (2023) Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion. SIAM/ASA Journal on Uncertainty Quantification 11(1): 320-357.
  • Klebanov I. and Wacker P. (2023) Maximum a posteriori estimators in ℓ p are well-defined for diagonal Gaussian priors. Inverse Problems 39(6)
  • Ashton G., Bernstein N., Buchner J., Chen X., Csányi G., Fowlie A., Feroz F., Griffiths M., Handley W. and Habeck M. (2022) Nested sampling for physical scientists. Nature Reviews Methods Primers 2(1)
  • Blömker D., Schillings C., Wacker P. and Weissmann S. (2022) CONTINUOUS TIME LIMIT OF THE STOCHASTIC ENSEMBLE KALMAN INVERSION: STRONG CONVERGENCE ANALYSIS. SIAM Journal on Numerical Analysis 60(6): 3181-3215.
  • Schillings C., Sprungk B. and Wacker P. (2020) On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems. Numerische Mathematik 145(4): 915-971.

I welcome inquiries regarding graduate student supervision, summer projects, and other ways of getting involved in doing mathematical and statistical research.  Please see my website for information about current and potential projects.