Udo Böhm

Udo Böhm is a researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His work bridges Bayesian statistics, computational psychology, and numerical mathematics through advanced modeling techniques.

• Research Focus: Bayesian inference, diffusion models, first-passage time analysis
• Technical Expertise: Numerical approximation of partial differential equations
• Collaborations: Extensive collaborations with psychologists and mathematicians

His publications demonstrate interdisciplinary applications of machine learning in psychological modeling and mathematical problem-solving. Current work involves developing anytime-valid confidence sequences and improving computational methods for diffusion processes.

Notable contributions include:

• Advancements in hierarchical diffusion decision models
• Efficient numerical algorithms for non-regular Fokker–Planck equations
• Foundational guidelines for Bayesian analysis in JASP