Jasper Marijn Everink

Jasper Marijn Everink is a Postdoc researcher at the Department of Applied Mathematics and Computer Science, Technical University of Denmark, specializing in computational uncertainty quantification and inverse problems. His work bridges statistical learning, regularization techniques, and imaging applications.

Research Interests: Everink focuses on Bayesian inference, sparse modeling, and spatial regularization methods. His research develops hierarchical frameworks for uncertainty quantification, leverages level-set techniques in electrical impedance tomography, and explores implicit prior modeling for inverse problems.

Publication Trends: Recent work emphasizes conformal prediction for imaging (2025), level-set regularization in tomography (2024), and sparsity-promoting priors (2024). Earlier contributions include projected density Bayesian methods (2023) and regularized Gaussian distributions for sparse inference (2023).

Collaborations: Collaborates across mathematics, computer science, and engineering disciplines, with applications in imaging and computational physics.