jangl13

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

Publications

2025

Jan Glaubitz, Hendrik Ranocha, Andrew Ross Winters, Michael Schlottke-Lakemper, Philipp Öffner, Gregor Gassner (2025) Generalized upwind summation-by-parts operators and their application to nodal discontinuous Galerkin methods Journal of Computational Physics, Vol. 529, Article 113841 (Article in journal) Continue to DOI
Jonathan Lindbloom, Jan Glaubitz, Anne Gelb (2025) Efficient sparsity-promoting MAP estimation for Bayesian linear inverse problems Inverse Problems, Vol. 41, Article 025001 (Article in journal) Continue to DOI

Organisation