Toni Karvonen


I am Academy of Finland Postdoctoral Researcher in the Department of Mathematics and Statistics at the University of Helsinki, Finland. My research is currently funded by the Academy project Scalable, adaptive and reliable probabilistic integration. Previously I have been

Research interests

I am interested in approximation theory, numerical analysis and probabilistic modelling. Most of my research focusses on theory and methodology for methods based on positive-definite kernels, such as Gaussian process regression and radial basis function interpolation, and approximation in reproducing kernel Hilbert spaces. I am particularly interested in using Hilbert space methods to analyse Gaussian process regression in interpolatory settings where the data are assumed noiseless. Bayesian cubature (an example of a probabilistic numerical method) for numerical integration is the method I have the most experience in. Topics that I am actively working on include

During my doctoral studies I worked on filtering methods for non-linear systems. [KBMS20] See also my full list of publications and Google Scholar profile.

Contact information

Street address:
Room B326, Exactum
Pietari Kalmin katu 5
00560 Helsinki, Finland