Photo of Jan Glaubitz

Jan Glaubitz

Assistant Professor

Bayesian Scientific Computing for hyperbolic conservation laws and inverse problems with uncertainty quantification.

About me

Welcome! I am an Assistant Professor in Scientific Computing in the Division of Applied Mathematics at Linköping University in Sweden.

I aim to advance foundational computational methodologies in Bayesian Scientific Computing, specifically at the intersection of numerical analysis, inverse problems, and uncertainty quantification. I strive to establish provable approximation, convergence, and stability results while quantifying the confidence in computational predictions.

Before joining Linköping University, I held postdoctoral positions at the Massachusetts Institute of Technology, where I was part of Youssef Marzouk’s UQ Group, and Dartmouth College, where I was supervised by Anne Gelb. I earned my PhD in Mathematics under the guidance of Thomas Sonar from the Technical University Braunschweig. My doctoral research focused on high-order numerical methods and shock-capturing techniques for hyperbolic conservation laws. (By now, I combine this with inverse problems, data assimilation, and uncertainty quantification.)

CV in brief

Since 2024:
Assistant Professor in Scientific Computing, Department of Mathematics, Linköping University, Sweden

2023 to 2024:
Postdoctoral Associate, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, USA (Supervisor: Youssef Marzouk)


2020 to 2023:
Postdoctoral Associate, Department of Mathematics, Dartmouth College, USA (Supervisor: Anne Gelb)

2016 to 2020:
PhD in Mathematics, Department of Mathematics, TU Braunschweig, Germany (Advisor: Thomas Sonar)

 

More information about me:
janglaubitz.com

Research

PhD students

Publications

2026

Jan Glaubitz, Youssef M. Marzouk (2026) Efficient Sampling for Sparse Bayesian Learning Using Hierarchical Prior Normalization SIAM/ASA Journal on Uncertainty Quantification, Vol. 14, p. 829-857 (Article in journal) https://dx.doi.org/10.1137/25m1790427
Mathieu Le Provost, Jan Glaubitz, Youssef Marzouk (2026) Preserving linear invariants in ensemble filtering methods Journal of Computational Physics, Vol. 563, Article 115048 (Article in journal) https://dx.doi.org/10.1016/j.jcp.2026.115048

2025

Jan Glaubitz, Jan Nordström, Philipp Öffner (2025) An Optimization-Based Construction Procedure for Function Space-Based Summation-by-Parts Operators on Arbitrary Grids Journal of Scientific Computing, Vol. 105, Article 83 (Article in journal) https://dx.doi.org/10.1007/s10915-025-03062-1
Jan Glaubitz, Jan Nordström, Philipp Öffner (2025) An Optimization-Based Construction Procedure for Function Space-Based Summation-by-Parts Operators on Arbitrary Grids Journal of Scientific Computing, Vol. 105, Article 83 (Article in journal) https://dx.doi.org/10.1007/s10915-025-03062-1
Hendrik Ranocha, Andrew R. Winters, Michael Schlottke-Lakemper, Philipp Öffner, Jan Glaubitz, Gregor J. Gassner (2025) On the robustness of high-order upwind summation-by-parts methods for nonlinear conservation laws Journal of Computational Physics, Vol. 520, p. 113471-113471, Article 113471 (Article in journal) https://dx.doi.org/10.1016/j.jcp.2024.113471

Organisation