I am an AI Scientist at Unilink Software, where I develop production AI systems spanning agentic AI, retrieval-augmented generation (RAG), optimisation, and explainable machine learning. Previously, I was a Machine Learning Engineer at Speechmatics, where I worked on large-scale multilingual speech recognition models and production ML systems.

Alongside my industrial work, I continue my research with the University of Warwick, where I completed my PhD and held a postdoctoral research position. My research focuses on probabilistic machine learning and Bayesian statistics, with particular interests in scalable Gaussian process inference, spatio-temporal modelling, multi-fidelity and multi-task learning, and incorporating physical structure into machine learning models. My work has been published at NeurIPS, ICML, ICLR, and AISTATS.

• Deep Gaussian Processes on Directed Acyclic Graphs

  Federico Perlino, Oliver Hamelijnck, Adam Johansen, Theodoros Damoulas

  Under review at The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPs 2026)

  [paper]
• MAGIC: Multi-Agent Generative Intention Coordination

  David Huk, Oliver Hamelijnck, Dimitris Demiris, Theodoros Damoulas

  MALGAI Workshop at The Fourteenth International Conference on Learning Representations (ICLR 2026)

  [paper]
• Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework

  Terje Mildner, Oliver Hamelijnck, Paris Giampouras, Theodoros Damoulas

  Forty-Second International Conference on Machine Learning (ICML 2025)

  [paper] [code]
• Physics-Informed Variational State-Space Gaussian Processes

  Oliver Hamelijnck, Arno Solin, Theodoros Damoulas

  The Thirty-Eighth Conference on Neural Information Processing Systems (NeurIPS 2024)

  [paper] [code]
• 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]