George Deligiannidis

Professor of Statistics
Director of MSc in Statistical Science
Department of Statistics, University of Oxford

Hugh Price Fellow in Statistics, Jesus College

Contact info

Email: deligian 'at' stats.ox.ac.uk
Telephone: 01865282855

Brief Bio

I studied mathematics (MMath) at Warwick university, then went on to study for a joint MSc in Financial Mathematics at Heriot-Watt University and the University of Edinburgh. I got my PhD from the University of Nottingham where I studied with Sergey Utev and Huiling Le. After my PhD I worked at the Department of Mathematics of the University of Leicester as Teaching Assistant and Teaching Fellow between 2009 and 2012. I moved to the Department of Statistics of the University of Oxford as a Departmental Lecturer, where I stayed until 2016 when I moved to King's College London as Lecturer in Statistics. I moved back to Oxford as Associate Professor of Statistics in late 2017. I was promoted to Professor of Statistics in August 2024.

News

September 30, 2024
New paper out with I. Azangulov and J. Rousseau, on the convergence of denoising diffusion models under the manifold hypothesis. The paper can be found here. We show that diffusion models achieve rates for score learning and sampling(in KL) independent of the ambient dimension, showing that they adapt to the underlying manifold structure.

Research

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, optimal transport and machine learning.

Publications

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link arXiv

Selected Talks

Cambridge, Statslab , April 2024
Warwick Stats Seminar , June 2023
Athens Probability Colloquium , March 2023
PRAIRIE Colloquium , December 2022
ESSEC Statistics Seminar , May 2022
AUEB (ΟΠΑ) Statistics Seminar , October 2021
Imperial Statistics Seminar, October 2021
Session on PDMPs and hypo-coercivity, MCQMC, Oxford, 2020
Mathematical Conversations, Institute for Advanced Study, Princeton, 2020
Bayescomp, Florida 2020
European Meeting of Statisticians, Palermo 2019
Probability Seminar, Warwick, February 2019.
SIAM Conference on High Dimensional Inference and Monte Carlo Techniques, Warwick 2019
Statistics Seminar, Bristol, October 2018.
Bayesian Computation for High-Dimensional Statistical Models, IMS, Singapore, September 2018.
Opening talk of Applied Mathematics Session, 3rd UK India Frontiers of Science Meeting, Royal Society, Chicheley Hall, May 2018.
BayesComp, Session on PDMPs, Barcelona, March 2018.
Machine Learning Seminar, Department of Information Technology, Uppsala, January 2018.
Department of Statistics, Warwick, June 2017.
Department of Statistics, Harvard University, May 2017.
Stochastic Analysis Seminar, Imperial College, December 2016.
Midlands Probability Seminar, University of Warwick, May 2016.
Mathematical Finance Seminar, Mathematical Institute, Oxford, March 2016.
Christmas Workshop on Sequential Monte Carlo, Imperial College, December 2015.
Probability Colloquium, Maxwell Institute, Edinburgh, September 2015.
Probability and Statistics Seminar, University of Bristol, March 2015.
Limit theorems in probability and dynamics, {CIRM, Marseilles}, July 2014.

Teaching

Advanced Simulation SC5 (2019-2020)

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Lectures

Monday, 16:00-17:00, LG01 Department of Statistics
Tuesday, 11-12:00, LG01 Department of Statistics.

Classes

Undergraduates:

Tuesday, weeks 2,5,7, TT1 13:30-15:00
Thursday, weeks 2,5,7, TT1 10-11:30

MSc

Tuesday, weeks 3, 5, 7, 8 16-17:00

Problem Sheets

Problem Sheet 0, this is a preparatory problem sheet covering some prerequisites.
Problem Sheet 1, Due Monday, wk 2 @ 9 a.m. (UG only).
Problem Sheet 2, Due Monday, wk 5 @ 9 a.m. (UG only).

Solutions to R exercises

Notes

These may be updated as we go.

Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7 (old reversible jump MCMC no longer in the syllabus)
Chapter 7
Chapter 8
Chapter 9

Slides

These will be updated as we go, however only minor changes will be made. Feel free to use these to prepare ahead of the lecture.

Disclaimer

Disclaimer This course is based on the material developed by previous instructors, including Arnaud Doucet, Pierre E. Jacob, Rémi Bardenet, George Deligiannidis, Lawrence M. Murray, Tigran Nagapetyan, Patrick Rebeschini and Paul Vanetti.

Modern Statistical Theory (StatML CDT)

Notes

Notes
Slides

SB21 Foundations of Statistical Inference

I will be updating these from time to time, but not very regularly. For the most up-to-date notes and problem sheets please visit the Canvas website.

Notes

Notes