Kasper Bågmark

I am a WASP-KAW Postdoctoral Fellow, funded through the Wallenberg AI, Autonomous Systems and Software Program and the Knut and Alice Wallenberg Foundation. I am based at Vrije Universiteit Amsterdam, where I work on uncertainty quantification for mathematical neuroscience with scientific machine learning, in collaboration with Frank van der Meulen and Daniele Avitabile.

My research interests span Bayesian statistics, stochastic analysis, numerical analysis, and scientific machine learning. I am particularly interested in probabilistic approaches to inverse problems, inference for complex models, and numerical schemes for high-dimensional partial and stochastic differential equations.

Beyond my research, I am active in the DYNSTOCH network on statistical methods for dynamical stochastic models, and chaired the most recent edition of the workshop, DYNSTOCH 2026, in Gothenburg in June 2026. I have also previously co-organized the Statistics Seminar at the Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg.

Proposals and collaboration

I am always interested in collaborative projects across the areas of my research. If you’re interested in working together on problems related to probabilistic learning, Bayesian statistics, uncertainty quantification, or differential equations (PDEs, SPDEs, or SDEs), please feel free to reach out.

For Master’s thesis students interested in nonlinear filtering, I have a proposal on deep BSDE methods for this problem. I am happy to discuss other thesis topics within my broader research interests.