I am a third year PhD student supervised by Theodoros DamoulasLink opens in a new window with a scholarship from the Alan Turing Institute. I am part of the London air quality projectLink opens in a new window at the Turing and the Warwick Machine Learning GroupLink opens in a new window. My research interests lie in Machine learning and spatio-temporal Statistics with a focus scalable inference methods for Bayesian methods such as Gaussian Processes. I am also interested in multi-fidelity and multi-task modelling, and incorporating structure through structured priors and physical laws.

Published Papers

• Spatio-temporal variational Gaussian processes, O Hamelijnck*, W Wilkinson*, N Loppi, A Solin, T Damoulas, NeurIPS 2021
• Transforming Gaussian Processes With Normalizing Flows, Maronas, J.*, Hamelijnck, O.*, Knoblauch, J., & Damoulas, T. , AISTATs 2021
• Non-separable Non-stationary random fields, K Wang, O Hamelijnck, T Damoulas, M Steel, ICML 2020
• Multi-resolution multi-task Gaussian processes, O Hamelijnck, T Damoulas, K Wang, M Girolami, NeurIPS 2019
  
  

Contact: ohamelijnck@turing.ac.uk

Website: https://ohamelijnck.github.io/