Professor George Deligiannidis

Associate Professor of Statistics

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.

News

I recently received a New Investigator Award from EPSRC. I will be hiring a postdoc for three years to work on this project. If interested, you can apply here .

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
Middleton, L., Deligiannidis, G., Doucet, A. and Jacob, P. (2020) “Unbiased Markov chain Monte Carlo for intractable target distributions”, Electronic Journal of Statistics, 14(2), pp. 2842–2891.
Schmon, S., Deligiannidis, G., Doucet, A. and Pitt, M. (2020) “Large sample asymptotics of the pseudo-marginal method”, Biometrika, 108(1), pp. 37–51.
Şimşekli, U., Sener, O., Deligiannidis, G. and Erdogdu, M. (2020) “Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks”, arXiv.
Faizi, F., Deligiannidis, G. and Rosta, E. (2020) “Efficient Irreversible Monte Carlo Samplers.”, Journal of chemical theory and computation, 16(4), pp. 2124–2138.
Cornish, R., Caterini, A., Deligiannidis, G. and Doucet, A. (2020) “Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows”, in INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 119.
Cornish, R., Vanetti, P., Bouchard-Côté, A., Deligiannidis, G. and Doucet, A. (2019) “Scalable Metropolis-Hastings for exact Bayesian inference with large datasets”, in Proceedings of Machine Learning Research. Journal of Machine Learning Research, pp. 1351–1360.
Schmon, S., Deligiannidis, G. and Doucet, A. (2019) “Bernoulli race particle filters”, in Proceedings of Machine Learning Research: 22nd International Conference on Artificial Intelligence and Statistics. MLR Press, pp. 2350–2358.
Middleton, L., Deligiannidis, G., Doucet, A. and Jacob, P. (2019) “Unbiased smoothing using Particle Independent Metropolis-Hastings”, in Proceedings of Machine Learning Research: 22nd International Conference on Artificial Intelligence and Statistics. MLR Press, pp. 2378–2387.
Deligiannidis, G., Bouchard-Cote, A. and Doucet, A. (2019) “Exponential ergodicity of the bouncy particle sampler”, Annals of Statistics, 47(3), pp. 1268–1287.
Cornish, R., Vanetti, P., Bouchard-Côté, A., Deligiannidis, G. and Doucet, A. (2019) “Scalable metropolis-hastings for exact Bayesian inference with large datasets”, in 36th International Conference on Machine Learning, ICML 2019, pp. 2398–2429.

Publications

Middleton, L., Deligiannidis, G., Doucet, A. and Jacob, P. (2020) “Unbiased Markov chain Monte Carlo for intractable target distributions”, Electronic Journal of Statistics, 14(2), pp. 2842–2891.
Schmon, S., Deligiannidis, G., Doucet, A. and Pitt, M. (2020) “Large sample asymptotics of the pseudo-marginal method”, Biometrika, 108(1), pp. 37–51.
Şimşekli, U., Sener, O., Deligiannidis, G. and Erdogdu, M. (2020) “Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks”, arXiv.
Faizi, F., Deligiannidis, G. and Rosta, E. (2020) “Efficient Irreversible Monte Carlo Samplers.”, Journal of chemical theory and computation, 16(4), pp. 2124–2138.
Cornish, R., Caterini, A., Deligiannidis, G. and Doucet, A. (2020) “Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows”, in INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 119.
Cornish, R., Vanetti, P., Bouchard-Côté, A., Deligiannidis, G. and Doucet, A. (2019) “Scalable Metropolis-Hastings for exact Bayesian inference with large datasets”, in Proceedings of Machine Learning Research. Journal of Machine Learning Research, pp. 1351–1360.
Schmon, S., Deligiannidis, G. and Doucet, A. (2019) “Bernoulli race particle filters”, in Proceedings of Machine Learning Research: 22nd International Conference on Artificial Intelligence and Statistics. MLR Press, pp. 2350–2358.
Middleton, L., Deligiannidis, G., Doucet, A. and Jacob, P. (2019) “Unbiased smoothing using Particle Independent Metropolis-Hastings”, in Proceedings of Machine Learning Research: 22nd International Conference on Artificial Intelligence and Statistics. MLR Press, pp. 2378–2387.
Deligiannidis, G., Bouchard-Cote, A. and Doucet, A. (2019) “Exponential ergodicity of the bouncy particle sampler”, Annals of Statistics, 47(3), pp. 1268–1287.
Cornish, R., Vanetti, P., Bouchard-Côté, A., Deligiannidis, G. and Doucet, A. (2019) “Scalable metropolis-hastings for exact Bayesian inference with large datasets”, in 36th International Conference on Machine Learning, ICML 2019, pp. 2398–2429.

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