Short CV

Postdoctoral researcher at the Department of Mathematics, Vrije Universiteit Amsterdam.

Previously, I was a PhD student at the Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, Chalmers University of Technology & University of Gothenburg, and within the research school of the Wallenberg AI, Autonomous Systems and Software Program (WASP).

Organization

• Main organizer of DYNSTOCH 2026, a workshop on statistical methods for dynamical stochastic models, taking place in Gothenburg in June 2026
• Co-organizer of the Statistics Seminar at the Department of Mathematical Sciences, Chalmers & University of Gothenburg

Education

• Ph.D. Mathematics, Chalmers University of Technology & University of Gothenburg, 2026. Thesis: “Learning to Estimate: Bayesian Filtering with Deep Density Methods”.
• M.Sc. Engineering Mathematics and Computational Science, Chalmers University of Technology, 2020. Thesis: “Approximation of non-stationary fractional Gaussian random fields”.
• Exchange semester in Mathematics, ETH Zürich (Eidgenössische Technische Hochschule Zürich).
• B.Sc. Mathematics, University of Gothenburg, 2018.

Funding and Awards

• Postdoctoral scholarship (WASP through Knut and Alice Wallenberg Foundation), 2026, 128,000 EUR. Project: “Uncertainty quantification for mathematical neuroscience with scientific machine learning”.
• DYNSTOCH 2026 organizing grants from Wenner-Gren Foundations, Royal Swedish Academy of Sciences, and Wilhelm och Martina Lundgrens Vetenskapsfond, 2025, total 230,000 SEK
• Best poster award at 29th Nordic Conference in Mathematical Statistics, 2023
• Travel grants from Stiftelsen Längmanska kulturfonden, Kungl. Vetenskaps- och Vitterhets-Samhället (KVVS), and Stiftelsen för Vetenskaplig Forskning och Utbildning i Matematik (SveFUM), 2026

Supervision and teaching

• Filip Rydin — Master’s thesis: Energy-Based Deep Splitting for Fast and Accurate Estimation of Filtering Densities (2024).
  Now at Chalmers University of Technology.

• Melker Bild — Master’s thesis: Adapting the Deep Backward Stochastic Differential Equation Method to Stochastic Filtering (2025).
  Now at Korts­ystem i Gislaved AB.

• MVE565 – Computational Methods for Stochastic Differential Equations
  Teaching assistant, Chalmers (2024).
  Course page

• TMA881 – High Performance Computing
  Teaching assistant, Chalmers (2020, 2021, 2022, 2023).
  Course page

• TMV206 – Linear Algebra
  Teaching assistant, Chalmers (2021).

• TMV216 – Linear Algebra
  Teaching assistant, Chalmers (2020).