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.)
Jan Glaubitz
Assistant Professor, Docent
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.
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
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
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
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
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
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