Biography

I am a postdoctoral researcher at the University of Warwick, working on the Machine Learning Foundations of Digital Twins project under the supervision of Theodoros Damoulas. My research focuses on Machine Learning and spatio-temporal Statistics, particularly scalable inference methods for non-parametric Bayesian models such as Gaussian Processes. I am also interested in multi-fidelity and multi-task modeling, with a focus on integrating structure through non-stationary, non-separable priors and physical laws.

Publications

Physics-Informed Variational State-Space Gaussian Processes

Oliver Hamelijnck*, Arno Solin, Theodoros Damoulas

To appear at the Thirty-Eighth Conference on Neural Information Processing Systems (NeurIPS 2024)

[paper]
Spatio-Temporal Variational Gaussian Processes

Oliver Hamelijnck*, William J. Wilkinson*, Niki A. Loppi, Arno Solin, Theodoros Damoulas

The Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021)

[paper] [code]
Transforming Gaussian processes with normalizing flows

Juan Maronãs*, Oliver Hamelijnck*, Jeremias Knoblauch, Theodoros Damoulas

The 24th International Conference on Artificial Intelligence and Statistics (AISTATs 2021)

[paper] [code]
Non-separable Non-stationary random fields

Kangrui Wang, Oliver Hamelijnck, Theodoros Damoulas, Mark Steel

The Thirty-seventh International Conference on Machine Learning (ICML 2020)

[paper] [code]
Multi-resolution Multi-task Gaussian Processes

Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark Girolami

The Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)

[paper] [code]