MAIN — PUBLICATIONS
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
- (2020–2021) Research Fellow on the Programme for Data-Centric Engineering at the Alan Turing Institute, London, UK, where I worked with Chris Oates and Mark Girolami.
- (2016–2020) Doctoral Student and Postdoctoral Researcher supervised by Simo Särkkä in the Department of Electrical Engineering and Automation at Aalto University, Espoo, Finland. I completed my doctoral degree in 2019.
- (2010–2015) Student in the Department of Mathematics and Statistics at the University of Helsinki, Finland. I obtained my Bachelor's (in mathematics) and Master's degrees (in applied mathematics) in 2015.
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
- Parameter estimation in Gaussian process regression. How do statistical parameter estimation methods, such as maximum likelihood estimation or cross-validation, behave in interpolatory settings? The problem has connections to sample path properties. [Kar21, KWTOS20 & KTS19]
- Reliability of Gaussian process regression. In which settings and to what extent can we trust the uncertainty quantification that Gaussian process models provide? [KWTOS20]
- Scalable and high-dimensional of Bayesian cubature. Kernel-based methods typically suffer from cubic computational complexity. How can Bayesian cubature, which uses Gaussian process modelling, be sped up and used in high-dimensional settings? [KSO19 & KS18]
- Integration in reproducing kernel Hilbert spaces. How fast do numerical integration methods converge in reproducing kernel Hilbert spaces? Which nodes should one use? How to approximate the worst-case optimal weights? I have been particularly interested in the Gaussian kernel. [KOM21 & SKH21]
Room B326, Exactum
Pietari Kalmin katu 5
00560 Helsinki, Finland