Professor George Deligiannidis

Professor of Statistics, Director of MSc in Statistical Science

Biographical Sketch

I studied Mathematics (MMath) at the University of Warwick and Financial Mathematics (MSc) at Heriot-Watt University and the University of Edinburgh. After obtaining my PhD from the School of Mathematical Sciences of the University of Nottingham under the supervision of Sergey Utev and Huiling Le, I moved to the Department of Mathematics of the University of Leicester as a Teaching Assistant/Fellow. In 2012 I moved to the Department of Statistics of the University of Oxford as Departmental Lecturer. I stayed in Oxford until September 2016 when I moved to the Department of Mathematics of King’s College London as Lecturer in Statistics. I moved back to the University of Oxford in December 2017 as Associate Professor of Statistics. I was promoted to Professor of Statistics in August 2024. 

Research Interests

I work in the intersection of probability and statistics to analyse random processes and objects, especially those arising from algorithms used in computational statistics and machine learning. I have worked extensively on the theory and methodology of sampling methods, especially Markov Chain Monte Carlo. I have also worked on random walks on lattices and groups.
At the moment I am particularly interested in the interplay between sampling, optimization and machine learning.

Publications

Personal website
Dupuis, B. et al. (2025) “Understanding the Generalization Error of Markov algorithms through Poissonization”, arXiv.
Clerico, E. et al. (2025) “Generalisation under gradient descent via deterministic PAC-Bayes”, in Proceedings of Machine Learning Research, pp. 349–389.
Deligiannidis, G. et al. (2024) “On importance sampling and independent Metropolis-Hastings with an unbounded weight function”, arXiv.
Potaptchik, P., Azangulov, I. and Deligiannidis, G. (2024) “Linear Convergence of Diffusion Models Under the Manifold Hypothesis”, arXiv.
Azangulov, I., Deligiannidis, G. and Rousseau, J. (2024) “Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions”, arXiv.
Dupuis, B. et al. (2024) “Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets”, Journal of machine learning research [Preprint].
Benton, J. et al. (2024) “Authors’ reply to the Discussion of ‘From denoising diffusions to denoising Markov models’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 86(2), pp. 335–339.
Benton, J. et al. (2024) “From denoising diffusions to denoising Markov models”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 86(2), pp. 286–301.
Benton, J., Deligiannidis, G. and Doucet, A. (2024) “Error bounds for flow matching methods”, Transactions on Machine Learning Research [Preprint].
Phillips, A. et al. (2024) “Particle Denoising Diffusion Sampler”, arXiv.

Publications

Dupuis, B. et al. (2025) “Understanding the Generalization Error of Markov algorithms through Poissonization”, arXiv.
Clerico, E. et al. (2025) “Generalisation under gradient descent via deterministic PAC-Bayes”, in Proceedings of Machine Learning Research, pp. 349–389.
Deligiannidis, G. et al. (2024) “On importance sampling and independent Metropolis-Hastings with an unbounded weight function”, arXiv.
Potaptchik, P., Azangulov, I. and Deligiannidis, G. (2024) “Linear Convergence of Diffusion Models Under the Manifold Hypothesis”, arXiv.
Azangulov, I., Deligiannidis, G. and Rousseau, J. (2024) “Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions”, arXiv.
Dupuis, B. et al. (2024) “Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets”, Journal of machine learning research [Preprint].
Benton, J. et al. (2024) “Authors’ reply to the Discussion of ‘From denoising diffusions to denoising Markov models’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 86(2), pp. 335–339.
Benton, J. et al. (2024) “From denoising diffusions to denoising Markov models”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 86(2), pp. 286–301.
Benton, J., Deligiannidis, G. and Doucet, A. (2024) “Error bounds for flow matching methods”, Transactions on Machine Learning Research [Preprint].
Phillips, A. et al. (2024) “Particle Denoising Diffusion Sampler”, arXiv.

Contact Details

College Affiliation: Hugh Price Fellow at Jesus College

Email: deligian@stats.ox.ac.uk

Telephone: +44(0)1865 282851

Office number: 1.05

Graduate Students

Joe Benton

Amitis Shidani

Eugenio Clerico

Chris Williams

Angus Philips

Guneet Dhillon

James Thornton 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology