Jan Glaubitz

Jan Glaubitz is an Assistant Professor in Scientific Computing at the Department of Mathematics, Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) within the Faculty of Science and Engineering. His research lies at the intersection of numerical analysis, inverse problems, uncertainty quantification, and computational fluid dynamics, with a focus on developing robust and provably convergent computational methods.

His research interests center on advancing Bayesian Scientific Computing, particularly for hyperbolic conservation laws and inverse problems under uncertainty. He works on establishing rigorous theoretical foundations—such as approximation, convergence, and stability—for numerical methods while quantifying confidence in computational predictions. His work integrates high-order numerical schemes, shock-capturing techniques, data assimilation, and uncertainty quantification to solve complex problems in science and engineering.

The recent publications highlight a strong trend in developing mathematically rigorous and computationally efficient methods for partial differential equations and Bayesian inference. His work bridges theoretical numerical analysis with practical computational frameworks, focusing on stability-preserving discretizations and sparse reconstructions in inverse problems.

Jan Glaubitz has held postdoctoral positions at the Massachusetts Institute of Technology (MIT) under Youssef Marzouk and at Dartmouth College under Anne Gelb, reflecting a strong international research trajectory. Although no specific grants or students are listed, his active publication record and leadership in method development suggest ongoing research funding and academic mentorship.

He is associated with the Division of Applied Mathematics at Linköping University, which conducts research in computational mathematics, mathematical statistics, and optimization, providing a collaborative environment for interdisciplinary scientific computing.